{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":10385,"databundleVersionId":298493,"sourceType":"competition"},{"sourceId":260043662,"sourceType":"kernelVersion"}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 导入库","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nfrom tqdm import tqdm\nimport numpy as np\nimport scipy.ndimage\nfrom sklearn.preprocessing import StandardScaler\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport os\nimport xgboost as xgb\nfrom sklearn.model_selection import StratifiedKFold\nimport joblib\nfrom sklearn.metrics import accuracy_score, roc_auc_score, classification_report, confusion_matrix, log_loss\nfrom xgboost import to_graphviz\nfrom sklearn.model_selection import train_test_split","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T08:59:23.685617Z","iopub.execute_input":"2025-09-16T08:59:23.685963Z","iopub.status.idle":"2025-09-16T08:59:23.692335Z","shell.execute_reply.started":"2025-09-16T08:59:23.685937Z","shell.execute_reply":"2025-09-16T08:59:23.691273Z"}},"outputs":[],"execution_count":19},{"cell_type":"markdown","source":"# 导入数据","metadata":{}},{"cell_type":"code","source":"train=pd.read_csv(\"/kaggle/input/santander-customer-transaction-prediction/train.csv\").drop(['ID_code'],axis=1)\ntest=pd.read_csv(\"/kaggle/input/santander-customer-transaction-prediction/test.csv\").drop(['ID_code'],axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T08:39:02.362364Z","iopub.execute_input":"2025-09-16T08:39:02.362778Z","iopub.status.idle":"2025-09-16T08:39:23.537746Z","shell.execute_reply.started":"2025-09-16T08:39:02.362625Z","shell.execute_reply":"2025-09-16T08:39:23.536285Z"}},"outputs":[],"execution_count":1},{"cell_type":"markdown","source":"# 找到伪造test数据集","metadata":{}},{"cell_type":"markdown","source":"我们发现在test中存在部分由原来数据合成的伪造test数据，通过统计每个数在该列中出现的次数，如果一整行数据都是非唯一值，那么说明该数据是由其他列拼凑起来的，默认其结果为0","metadata":{}},{"cell_type":"code","source":"te_=test.values\n#如果一行数据中的每一列的数据在其他行中都有出现过说明这行数据是伪造数据\nunique_samples = []\nunique_count = np.zeros_like(te_)\nfor feature in tqdm(range(te_.shape[1])):\n    _, index_, count_ = np.unique(te_[:, feature], return_counts=True, return_index=True)\n    unique_count[index_[count_ == 1], feature] += 1\n\n# Samples which have unique values are real the others are fake\nreal_samples_indexes = np.argwhere(np.sum(unique_count, axis=1) > 0)[:, 0]\nsynthetic_samples_indexes = np.argwhere(np.sum(unique_count, axis=1) == 0)[:, 0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T08:40:42.403392Z","iopub.execute_input":"2025-09-16T08:40:42.404132Z","iopub.status.idle":"2025-09-16T08:40:49.398306Z","shell.execute_reply.started":"2025-09-16T08:40:42.404094Z","shell.execute_reply":"2025-09-16T08:40:49.397041Z"}},"outputs":[{"name":"stderr","text":"100%|██████████| 200/200 [00:06<00:00, 29.42it/s]\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"X_test=test.iloc[real_samples_indexes,:]\nX_fake=test.iloc[synthetic_samples_indexes,:]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T08:40:59.619074Z","iopub.execute_input":"2025-09-16T08:40:59.619368Z","iopub.status.idle":"2025-09-16T08:40:59.815542Z","shell.execute_reply.started":"2025-09-16T08:40:59.619345Z","shell.execute_reply":"2025-09-16T08:40:59.814596Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"features = [c for c in train.columns if c not in ['ID_code', 'target']]\nX_train=train.drop(['target'],axis=1)\ntarget_train=train['target']\nX_all = pd.concat([X_train, X_test])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T08:41:08.679945Z","iopub.execute_input":"2025-09-16T08:41:08.680231Z","iopub.status.idle":"2025-09-16T08:41:09.048480Z","shell.execute_reply.started":"2025-09-16T08:41:08.680210Z","shell.execute_reply":"2025-09-16T08:41:09.047209Z"}},"outputs":[],"execution_count":6},{"cell_type":"markdown","source":"# 预处理","metadata":{}},{"cell_type":"markdown","source":"## 删除低相关性和低重要性的特征（弃用）","metadata":{}},{"cell_type":"code","source":"# drop_vars = [7,\n#             10,\n#             17,\n#             27,\n#             29,\n#             30,\n#             38,\n#             41,\n#             46,\n#             96,\n#             100,\n#             103,\n#             126,\n#             158,\n#             185]\n\n# var_len = 200 - len(drop_vars)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 简单计数编码（弃用）","metadata":{}},{"cell_type":"code","source":"def count_encode_and_merge(df: pd.DataFrame, target_column) -> pd.DataFrame:\n    \"\"\"\n    对每个特征列进行计数编码，并将计数编码后的列与原始数据合并。\n\n    参数:\n    ----\n    df : DataFrame\n        输入的原始数据表（不包含目标列）。\n    target_column : str, optional\n        目标列的名称（默认值为 None）。如果提供，则从 `df` 中删除目标列。\n\n    返回:\n    ----\n    DataFrame\n        合并后的数据，包括原始数据和计数编码后的列。\n    \"\"\"\n    # 如果指定了 target_column，删除该列\n    if target_column:\n        df = df.drop(columns=[target_column])\n    \n    # 对每一列进行计数编码\n    count_encoded_df = df.apply(lambda col: col.map(col.value_counts()))\n    \n    # 将计数编码后的列与原数据合并\n    df_encoded = pd.concat([df, count_encoded_df.add_suffix('_count')], axis=1)\n    \n    return df_encoded","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# train_x_encoded = count_encode_and_merge(train_x, target_column=None)\n# test_encoded = count_encode_and_merge(test, target_column=None)\n# train_x_encoded.columns = train_x_encoded.columns.astype(str)\n# test_encoded.columns = test_encoded.columns.astype(str)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-12T02:07:12.893583Z","iopub.execute_input":"2025-09-12T02:07:12.893986Z","iopub.status.idle":"2025-09-12T02:07:21.414345Z","shell.execute_reply.started":"2025-09-12T02:07:12.893960Z","shell.execute_reply":"2025-09-12T02:07:21.413717Z"}},"outputs":[],"execution_count":7},{"cell_type":"markdown","source":"## 计数加高斯平滑密度以及偏差","metadata":{}},{"cell_type":"code","source":"sigma_fac = 0.001\nsigma_base = 4\n\neps = 0.00000001\n\ndef get_count(X_all, X_fake):\n    features_count = np.zeros((X_all.shape[0], len(features)))\n    features_density = np.zeros((X_all.shape[0], len(features)))\n    features_deviation = np.zeros((X_all.shape[0], len(features)))\n\n    features_count_fake = np.zeros((X_fake.shape[0], len(features)))\n    features_density_fake = np.zeros((X_fake.shape[0], len(features)))\n    features_deviation_fake = np.zeros((X_fake.shape[0], len(features)))\n    \n    sigmas = []\n\n    for i,var in enumerate(tqdm(features)):\n        X_all_var_int = (X_all[var].values * 10000).round().astype(int)\n        X_fake_var_int = (X_fake[var].values * 10000).round().astype(int)\n        lo = X_all_var_int.min()\n        X_all_var_int -= lo\n        X_fake_var_int -= lo\n        hi = X_all_var_int.max()+1\n        counts_all = np.bincount(X_all_var_int, minlength=hi).astype(float)\n        zeros = (counts_all == 0).astype(int)\n        before_zeros = np.concatenate([zeros[1:],[0]])\n        indices_all = np.arange(counts_all.shape[0])\n        # Geometric mean of twice sigma_base and a sigma_scaled which is scaled to the length of array \n        sigma_scaled = counts_all.shape[0]*sigma_fac\n        sigma = np.power(sigma_base * sigma_base * sigma_scaled, 1/3)\n        sigmas.append(sigma)\n        counts_all_smooth = scipy.ndimage.filters.gaussian_filter1d(counts_all, sigma)\n        deviation = counts_all / (counts_all_smooth+eps)\n        indices = X_all_var_int\n        features_count[:,i] = counts_all[indices]\n        features_density[:,i] = counts_all_smooth[indices]\n        features_deviation[:,i] = deviation[indices]\n        indices_fake = X_fake_var_int\n        features_count_fake[:,i] = counts_all[indices_fake]\n        features_density_fake[:,i] = counts_all_smooth[indices_fake]\n        features_deviation_fake[:,i] = deviation[indices_fake]\n        \n    features_count_names = [var+'_count' for var in features]\n    features_density_names = [var+'_density' for var in features]\n    features_deviation_names = [var+'_deviation' for var in features]\n\n    X_all_count = pd.DataFrame(columns=features_count_names, data = features_count)\n    X_all_count.index = X_all.index\n    X_all_density = pd.DataFrame(columns=features_density_names, data = features_density)\n    X_all_density.index = X_all.index\n    X_all_deviation = pd.DataFrame(columns=features_deviation_names, data = features_deviation)\n    X_all_deviation.index = X_all.index\n    X_all = pd.concat([X_all,X_all_count, X_all_density, X_all_deviation], axis=1)\n    \n    X_fake_count = pd.DataFrame(columns=features_count_names, data = features_count_fake)\n    X_fake_count.index = X_fake.index\n    X_fake_density = pd.DataFrame(columns=features_density_names, data = features_density_fake)\n    X_fake_density.index = X_fake.index\n    X_fake_deviation = pd.DataFrame(columns=features_deviation_names, data = features_deviation_fake)\n    X_fake_deviation.index = X_fake.index\n    X_fake = pd.concat([X_fake,X_fake_count, X_fake_density, X_fake_deviation], axis=1)    \n\n    features_count = features_count_names\n    features_density = features_density_names\n    features_deviation = features_deviation_names\n    return X_all, features_count, features_density, features_deviation, X_fake\n\nX_all, features_count, features_density, features_deviation, X_fake = get_count(X_all, X_fake)\nprint(X_all.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T08:49:34.102619Z","iopub.execute_input":"2025-09-16T08:49:34.103136Z","iopub.status.idle":"2025-09-16T08:49:55.288375Z","shell.execute_reply.started":"2025-09-16T08:49:34.103108Z","shell.execute_reply":"2025-09-16T08:49:55.287149Z"}},"outputs":[{"name":"stderr","text":"  0%|          | 0/200 [00:00<?, ?it/s]/tmp/ipykernel_36/2425362682.py:32: DeprecationWarning: Please import `gaussian_filter1d` from the `scipy.ndimage` namespace; the `scipy.ndimage.filters` namespace is deprecated and will be removed in SciPy 2.0.0.\n  counts_all_smooth = scipy.ndimage.filters.gaussian_filter1d(counts_all, sigma)\n100%|██████████| 200/200 [00:15<00:00, 12.94it/s]\n","output_type":"stream"},{"name":"stdout","text":"(300000, 800)\n","output_type":"stream"}],"execution_count":9},{"cell_type":"markdown","source":"# 标准化","metadata":{}},{"cell_type":"code","source":"features_to_scale = [features, features_count]\n\nfrom sklearn.preprocessing import StandardScaler\n\ndef get_standardized(X_all, X_fake):\n    scaler = StandardScaler()\n    features_to_scale_flatten = [var for sublist in features_to_scale for var in sublist]\n    scaler.fit(X_all[features_to_scale_flatten])\n    features_scaled = scaler.transform(X_all[features_to_scale_flatten])\n    features_scaled_fake = scaler.transform(X_fake[features_to_scale_flatten])\n    X_all[features_to_scale_flatten] = features_scaled\n    X_fake[features_to_scale_flatten] = features_scaled_fake\n    return X_all, X_fake\n\nX_all, X_fake = get_standardized(X_all, X_fake)\n\nprint(X_all.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T08:49:59.299603Z","iopub.execute_input":"2025-09-16T08:49:59.300325Z","iopub.status.idle":"2025-09-16T08:50:04.488567Z","shell.execute_reply.started":"2025-09-16T08:49:59.300292Z","shell.execute_reply":"2025-09-16T08:50:04.487599Z"}},"outputs":[{"name":"stdout","text":"(300000, 800)\n","output_type":"stream"}],"execution_count":11},{"cell_type":"code","source":"train_length=200000\nX_train = X_all.iloc[:train_length,:]\nX_test = X_all.iloc[train_length:,:]\ndel X_all\nimport gc\ngc.collect()\nprint(X_train.shape, X_test.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T08:50:04.489837Z","iopub.execute_input":"2025-09-16T08:50:04.490232Z","iopub.status.idle":"2025-09-16T08:50:04.597429Z","shell.execute_reply.started":"2025-09-16T08:50:04.490209Z","shell.execute_reply":"2025-09-16T08:50:04.596330Z"}},"outputs":[{"name":"stdout","text":"(200000, 800) (100000, 800)\n","output_type":"stream"}],"execution_count":12},{"cell_type":"markdown","source":"# 一些可视化函数","metadata":{}},{"cell_type":"code","source":"#查看划分的比例\ndef plot_class_distribution(y, title=\"Class Distribution\",save_path=None):\n    \"\"\"\n    可视化二分类数据的数量和比例\n    :param y: 二分类目标变量 (pd.Series 或 list)\n    :param title: 图表标题\n    \"\"\"\n    # 转换为 Series\n    if not isinstance(y, pd.Series):\n        y = pd.Series(y)\n\n    # 统计数量和比例\n    class_counts = y.value_counts()\n    class_percent = y.value_counts(normalize=True) * 100\n\n    # 合并到一个 DataFrame 方便展示\n    df = pd.DataFrame({\"Count\": class_counts, \"Percentage\": class_percent.round(2)})\n\n    # 绘制柱状图\n    plt.figure(figsize=(6, 4))\n    sns.barplot(x=df.index, y=df[\"Count\"], palette=\"Blues_d\")\n    \n    # 在柱子上显示数量和比例\n    for i, (count, pct) in enumerate(zip(df[\"Count\"], df[\"Percentage\"])):\n        plt.text(i, count + 0.5, f\"{count} ({pct}%)\", ha=\"center\")\n\n    plt.title(title)\n    plt.xlabel(\"Class\")\n    plt.ylabel(\"Count\")\n    plt.xticks(rotation=0)\n    plt.show()\n    if save_path:\n        plt.savefig(f\"{save_path}/{title}\")\n    \n\nplot_class_distribution(target_train, \"Train Target Distribution\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T08:50:32.309646Z","iopub.execute_input":"2025-09-16T08:50:32.310186Z","iopub.status.idle":"2025-09-16T08:50:32.632577Z","shell.execute_reply.started":"2025-09-16T08:50:32.310150Z","shell.execute_reply":"2025-09-16T08:50:32.631738Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":14},{"cell_type":"code","source":"def evaluate_binary_classifier(\n    y_true, y_pred, y_proba, model_name, \n    label_names=('Class 0', 'Class 1'), title_suffix='(Validation Set)', \n    save_results=True\n):\n    \"\"\"\n    二分类评估工具函数：打印Accuracy/AUC/分类报告，并绘制混淆矩阵\n    :param y_true: 真实标签（验证集）\n    :param y_pred: 预测标签（由模型predict得到）\n    :param y_proba: 正类概率（由模型predict_proba得到的[:,1]）\n    :param model: 训练好的模型\n    :param model_name: 模型名称（用于文件夹命名和保存模型）\n    :param label_names: 混淆矩阵坐标轴的类别名称\n    :param title_suffix: 图标题后缀，便于标注是验证集/测试集\n    :param save_results: 是否保存评估结果、混淆矩阵图和模型，默认保存\n    :return: 指标字典\n    \"\"\"\n    \n    # 创建保存目录\n    save_dir = f\"/kaggle/working/{model_name}_evaluation\"\n    if save_results and not os.path.exists(save_dir):\n        os.makedirs(save_dir)\n\n    # 评估指标计算\n    acc = accuracy_score(y_true, y_pred)\n    auc = roc_auc_score(y_true, y_proba)\n    \n    # 打印数值指标\n    print(f\"\\n=== {title_suffix} ===\")\n    print(f\"Accuracy: {acc:.4f}\")\n    print(f\"AUC: {auc:.4f}\")\n    print(\"\\n分类报告:\")\n    print(classification_report(y_true, y_pred))\n    \n    # 如果需要保存评估结果\n    if save_results:\n        # 保存评估指标到txt文件\n        with open(f\"{save_dir}/{title_suffix}_evaluation_results.txt\", \"w\") as f:\n            f.write(f\"=== {title_suffix} ===\\n\")\n            f.write(f\"Accuracy: {acc:.4f}\\n\")\n            f.write(f\"AUC: {auc:.4f}\\n\")\n            f.write(\"\\n分类报告:\\n\")\n            f.write(classification_report(y_true, y_pred))\n        \n        # 保存混淆矩阵图\n        plt.figure(figsize=(8, 6))\n        cm = confusion_matrix(y_true, y_pred)\n        sns.heatmap(\n            cm, annot=True, fmt='d', cmap='Blues',\n            xticklabels=label_names,\n            yticklabels=label_names\n        )\n        plt.title(f'Confusion Matrix {title_suffix}')\n        plt.ylabel('True Label')\n        plt.xlabel('Predicted Label')\n        plt.savefig(f\"{save_dir}/{title_suffix}_confusion_matrix.png\")\n        plt.close()\n\n        # 保存模型\n        # joblib.dump(model, f\"{save_dir}/model_{title_suffix}.joblib\")\n\n        #保存标签比例\n        result = y_pred.astype(int)\n        plot_class_distribution(result, f\"{model_name} {title_suffix} Target Distribution\",save_dir)\n        \n    \n    # 总是展示混淆矩阵\n    plt.figure(figsize=(8, 6))\n    cm = confusion_matrix(y_true, y_pred)\n    sns.heatmap(\n        cm, annot=True, fmt='d', cmap='Blues',\n        xticklabels=label_names,\n        yticklabels=label_names\n    )\n    plt.title(f'Confusion Matrix {title_suffix}')\n    plt.ylabel('True Label')\n    plt.xlabel('Predicted Label')\n    plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T09:37:22.028175Z","iopub.execute_input":"2025-09-16T09:37:22.028487Z","iopub.status.idle":"2025-09-16T09:37:22.040441Z","shell.execute_reply.started":"2025-09-16T09:37:22.028467Z","shell.execute_reply":"2025-09-16T09:37:22.039313Z"}},"outputs":[],"execution_count":27},{"cell_type":"code","source":"def plot_feature_importance(\n    model, \n    feature_names=None, \n    top_n=20, \n    importance_type='gain', \n    title='Top Feature Importance',\n    save_path=None\n):\n    \"\"\"\n    通用特征重要性可视化函数（支持XGBoost、CatBoost和LightGBM）\n    :param model: 训练好的 XGBClassifier / CatBoostClassifier / LGBMClassifier\n    :param feature_names: 特征名列表（默认自动生成）\n    :param top_n: 展示前N个特征\n    :param importance_type: \n        - XGBoost: 'weight'/'gain'/'cover'\n        - LightGBM: 'split'/'gain'\n    :param title: 图标题\n    :param save_path: 保存路径（如 'feature_importance.png'），为 None 时不保存\n    \"\"\"\n\n    # ===== XGBoost =====\n    if hasattr(model, \"get_booster\"):  \n        plt.figure(figsize=(12, 8))\n        xgb.plot_importance(model, max_num_features=top_n, importance_type=importance_type)\n        plt.title(title)\n\n    # ===== CatBoost =====\n    elif hasattr(model, \"get_feature_importance\"):  \n        importances = model.get_feature_importance()\n        if feature_names is None:\n            feature_names = [f'feat_{i}' for i in range(len(importances))]\n        idx = np.argsort(importances)[::-1][:top_n]\n        plt.figure(figsize=(12, 8))\n        plt.barh([feature_names[i] for i in idx][::-1], np.array(importances)[idx][::-1])\n        plt.title(title)\n        plt.xlabel('Importance')\n        plt.ylabel('Feature')\n        plt.tight_layout()\n\n    # ===== LightGBM =====\n    elif hasattr(model, \"feature_importances_\"):  \n        importances = model.feature_importances_\n        if feature_names is None:\n            feature_names = [f'feat_{i}' for i in range(len(importances))]\n        idx = np.argsort(importances)[::-1][:top_n]\n        plt.figure(figsize=(12, 8))\n        plt.barh([feature_names[i] for i in idx][::-1], np.array(importances)[idx][::-1])\n        plt.title(title)\n        plt.xlabel('Importance')\n        plt.ylabel('Feature')\n        plt.tight_layout()\n\n    else:\n        raise ValueError(\"Unsupported model type: only XGBoost, CatBoost and LightGBM are supported.\")\n\n    # 保存或显示\n    if save_path:\n        save_dir = os.path.dirname(save_path)\n        if save_dir and not os.path.exists(save_dir):\n            os.makedirs(save_dir)\n        plt.savefig(save_path, dpi=300, bbox_inches='tight')\n        print(f\"特征重要性图已保存到: {save_path}\")\n        plt.show()\n        plt.close()\n        \n    else:\n        plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T08:51:06.687952Z","iopub.execute_input":"2025-09-16T08:51:06.688300Z","iopub.status.idle":"2025-09-16T08:51:06.699955Z","shell.execute_reply.started":"2025-09-16T08:51:06.688273Z","shell.execute_reply":"2025-09-16T08:51:06.699027Z"}},"outputs":[],"execution_count":16},{"cell_type":"markdown","source":"# 单独训练特征集成","metadata":{}},{"cell_type":"markdown","source":"由于通过相关性计算发现每一列直接的相关性都极其低，所以我们采用了单独训练每个特征最后进行集成的策略，使得每个特征得到充分运用，同时避免伪交互的发生，在第二种编码的基础上在public上得到了的评分","metadata":{}},{"cell_type":"code","source":"params = {\n    'booster': 'gbtree',          # 对应 lightgbm 的 'boost': 'gbdt'\n    'objective': 'binary:logistic', # 对应 'binary'\n    'eval_metric': 'logloss',     # 对应 'binary_logloss'\n    'verbosity': 1,               # 输出等级，和 lgbm 一样\n    'learning_rate': 0.08,        # 对应 lightgbm 的 learning_rate\n    'max_depth': 2,              # xgboost 中 -1 不常用，一般用 None 或者指定正整数\n    'num_parallel_tree': 1,       # 保持单棵树提升\n    'reg_alpha': 2,               # L1 正则\n    'reg_lambda': 0,              # L2 正则\n    'max_bin': 256,               # 与 lgbm 类似，但 xgboost 默认通过 histogram 算法控制\n    'subsample': 0.7,               # 行采样比例（对应 lgbm 的 bagging_fraction）\n    'nthread': 8,                 # 对应 num_threads\n}\n\n# max_bin\nmax_bin_values = [256, 512, 1024]\nmax_bin_var = [0, 0, 1, 0, 0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 1, 1, 1, 0, 0, 0, 0, 2, 2, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 2, 1, 1, 1, 1, 0, 0, 0, 0, 1, 2, 1, 0, 0, 1, 2, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 2, 0, 1, 1, 0, 1, 0, 0, 1, 2, 1, 2, 1, 0, 0, 1, 0, 2, 0, 1, 0, 0, 2, 1, 1, 1, 0, 0, 0, 2, 0, 0, 2, 1, 0, 0, 1, 0, 1, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 1, 1, 2, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 0, 1, 0, 1, 1, 0, 2, 1, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 0, 1, 2, 0, 0, 0, 0, 2, 0, 0, 2, 0, 1, 1, 0, 2, 0, 0, 0, 1, 2, 0, 0, 1, 0, 2]\n\n# learning_rate\nlearning_rate_values = [0.06, 0.08, 0.12]\nlearning_rate_var = [2, 2, 2, 1, 2, 2, 2, 0, 1, 2, 0, 2, 2, 2, 0, 2, 2, 0, 2, 1, 2, 2, 2, 2, 2, 0, 1, 0, 2, 0, 0, 2, 0, 2, 2, 2, 1, 2, 0, 0, 2, 0, 0, 1, 2, 1, 2, 0, 0, 2, 1, 2, 2, 2, 2, 0, 0, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 1, 0, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 0, 2, 0, 2, 0, 2, 1, 0, 0, 1, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 1, 0, 2, 1, 2, 2, 1, 2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 1, 2, 1, 0, 2, 1, 1, 2, 2, 2, 2, 0, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 0, 1, 2, 0, 2, 2, 0, 1, 2, 2, 2, 1, 0, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1, 0, 0, 2, 0, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T08:59:31.523908Z","iopub.execute_input":"2025-09-16T08:59:31.524292Z","iopub.status.idle":"2025-09-16T08:59:31.539899Z","shell.execute_reply.started":"2025-09-16T08:59:31.524268Z","shell.execute_reply":"2025-09-16T08:59:31.538778Z"}},"outputs":[],"execution_count":20},{"cell_type":"code","source":"n_folds = 5\nearly_stopping_rounds = 10\nsettings = [4]\nnp.random.seed(47)\n\nsettings_best_ind = []\nfeatures_used = [features, features_count]  # 与原始代码一致\n\ndef train_trees():\n    preds_oof = np.zeros((len(X_train), len(features)))\n    preds_test = np.zeros((len(X_test), len(features)))\n    preds_train = np.zeros((len(X_train), len(features)))\n    preds_fake = np.zeros((len(X_fake), len(features)))\n\n    features_used_flatten = [var for sublist in features_used for var in sublist]\n    X_train_used = X_train[features_used_flatten]\n    X_test_used  = X_test[features_used_flatten]\n    X_fake_used  = X_fake[features_used_flatten]\n\n    for i in range(len(features)):\n        # —— 动态设置每列对应的超参——\n        params['max_bin']       = max_bin_values[max_bin_var[i]]\n        params['learning_rate'] = learning_rate_values[learning_rate_var[i]]\n\n        features_train = [feature_set[i] for feature_set in features_used]\n        print(f'Training on: {features_train}')\n\n        folds = StratifiedKFold(n_splits=n_folds, shuffle=True,\n                                random_state=np.random.randint(100000))\n        list_folds = list(folds.split(X_train_used.values, target_train.values))\n\n        preds_oof_temp   = np.zeros((preds_oof.shape[0], len(settings)))\n        preds_test_temp  = np.zeros((preds_test.shape[0], len(settings)))\n        preds_train_temp = np.zeros((preds_train.shape[0], len(settings)))\n        preds_fake_temp  = np.zeros((preds_fake.shape[0], len(settings)))\n\n        scores = []\n        for j, setting in enumerate(settings):\n            # 这里如需配合 setting 改其他参数，可在此处设定\n            print('\\nsetting: ', setting)\n\n            for k, (trn_idx, val_idx) in enumerate(list_folds):\n                print(\"Fold: {}\".format(k+1), end=\"\")\n\n                X_trn = X_train_used.iloc[trn_idx][features_train]\n                y_trn = target_train.iloc[trn_idx]\n                X_val = X_train_used.iloc[val_idx][features_train]\n                y_val = target_train.iloc[val_idx]\n\n                dtr  = xgb.DMatrix(X_trn, label=y_trn)\n                dval = xgb.DMatrix(X_val, label=y_val)\n                dte  = xgb.DMatrix(X_test_used[features_train])\n                dtr_full = xgb.DMatrix(X_train_used.iloc[trn_idx][features_train])\n                dfk  = xgb.DMatrix(X_fake_used[features_train])\n\n                clf = xgb.train(\n                    params,\n                    dtrain=dtr,\n                    num_boost_round=2000,\n                    evals=[(dtr, 'train'), (dval, 'valid')],\n                    early_stopping_rounds=early_stopping_rounds,\n                    verbose_eval=False\n                )\n\n                # 使用 best_ntree_limit 进行预测\n                iteration_kwargs = {}\n                best_iter = getattr(clf, \"best_iteration\", None)\n                if best_iter is not None:\n                    # xgboost >= 1.6 推荐用 iteration_range，右开区间所以 +1\n                    iteration_kwargs[\"iteration_range\"] = (0, best_iter + 1)\n                else:\n                    best_ntree_limit = getattr(clf, \"best_ntree_limit\", None)\n                    if best_ntree_limit is not None:\n                        # 旧版本用 ntree_limit\n                        iteration_kwargs[\"ntree_limit\"] = best_ntree_limit\n                    # 若两个都没有，就用默认（全树）预测\n                \n                prediction_val1   = clf.predict(dval,     **iteration_kwargs)\n                prediction_test1  = clf.predict(dte,      **iteration_kwargs)\n                prediction_train1 = clf.predict(dtr_full, **iteration_kwargs)\n                prediction_fake1  = clf.predict(dfk,      **iteration_kwargs)\n\n                # 验证集/训练集打分\n                s1     = roc_auc_score(y_val, prediction_val1)\n                s1_log = log_loss(y_val, prediction_val1)\n                print(' - val AUC: {:<8.4f} - loss: {:<8.3f}'.format(s1, s1_log*1000), end='')\n\n                s1_train     = roc_auc_score(y_trn, prediction_train1)\n                s1_log_train = log_loss(y_trn, prediction_train1)\n                print(' - train AUC: {:<8.4f} - loss: {:<8.3f}'.format(s1_train, s1_log_train*1000), end='')\n                print('')\n\n                # 与原逻辑一致的变换与累计\n                preds_oof_temp[val_idx, j]   += np.sqrt(prediction_val1   - prediction_val1.mean()   + 0.1)\n                preds_test_temp[:, j]        += np.sqrt(prediction_test1  - prediction_test1.mean()  + 0.1) / n_folds\n                preds_train_temp[trn_idx, j] += np.sqrt(prediction_train1 - prediction_train1.mean() + 0.1) / (n_folds - 1)\n                preds_fake_temp[:, j]        += np.sqrt(prediction_fake1  - prediction_fake1.mean()  + 0.1) / n_folds\n\n            # 以与你原始代码一致的方式汇总并打分\n            score_setting      = roc_auc_score(target_train, preds_oof_temp[:, j])\n            score_setting_log  = 1000 * log_loss(target_train, np.exp(preds_oof_temp[:, j]))\n            scores.append(score_setting_log)\n            print(\"Score:  - val AUC: {:<8.4f} - loss: {:<8.3f}\".format(score_setting, score_setting_log), end='')\n\n            score_setting_train     = roc_auc_score(target_train, preds_train_temp[:, j])\n            score_setting_log_train = 1000 * log_loss(target_train, np.exp(preds_train_temp[:, j]))\n            print(\" - train AUC: {:<8.4f} - loss: {:<8.3f}\".format(score_setting_train, score_setting_log_train))\n\n        best_ind = np.argmin(scores)\n        settings_best_ind.append(best_ind)\n        preds_oof[:, i]   = preds_oof_temp[:,  best_ind]\n        preds_test[:, i]  = preds_test_temp[:, best_ind]\n        preds_train[:, i] = preds_train_temp[:,best_ind]\n        preds_fake[:, i]  = preds_fake_temp[:, best_ind]\n\n        print('\\nbest setting: ', settings[best_ind])\n        preds_oof_cum = preds_oof[:, :i+1].mean(axis=1)\n        print(\"Cum CV val  : {:<8.4f} - loss: {:<8.3f}\".format(\n            roc_auc_score(target_train, preds_oof_cum),\n            1000 * log_loss(target_train, np.exp(preds_oof_cum))\n        ))\n        preds_train_cum = preds_train[:, :i+1].mean(axis=1)\n        print(\"Cum CV train: {:<8.4f} - loss: {:<8.3f}\".format(\n            roc_auc_score(target_train, preds_train_cum),\n            1000 * log_loss(target_train, np.exp(preds_train_cum))\n        ))\n        print('*****' * 10 + '\\n')\n\n    return preds_oof, preds_test, preds_train, preds_fake\n\npreds_oof, preds_test, preds_train, preds_fake = train_trees()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T09:02:09.134903Z","iopub.execute_input":"2025-09-16T09:02:09.135248Z","iopub.status.idle":"2025-09-16T09:34:34.748707Z","shell.execute_reply.started":"2025-09-16T09:02:09.135225Z","shell.execute_reply":"2025-09-16T09:34:34.747607Z"}},"outputs":[{"name":"stdout","text":"Training on: ['var_0', 'var_0_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5527   - loss: 323.478  - train AUC: 0.5513   - loss: 323.754 \nFold: 2 - val AUC: 0.5465   - loss: 323.606  - train AUC: 0.5532   - loss: 323.733 \nFold: 3 - val AUC: 0.5474   - loss: 323.793  - train AUC: 0.5571   - loss: 323.549 \nFold: 4 - val AUC: 0.5447   - loss: 324.382  - train AUC: 0.5546   - loss: 323.501 \nFold: 5 - val AUC: 0.5488   - loss: 323.937  - train AUC: 0.5576   - loss: 323.480 \nScore:  - val AUC: 0.5478   - loss: 32421.627 - train AUC: 0.5563   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.5478   - loss: 32421.627\nCum CV train: 0.5563   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_1', 'var_1_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5475   - loss: 323.608  - train AUC: 0.5531   - loss: 323.680 \nFold: 2 - val AUC: 0.5462   - loss: 323.708  - train AUC: 0.5505   - loss: 323.776 \nFold: 3 - val AUC: 0.5456   - loss: 324.284  - train AUC: 0.5511   - loss: 323.594 \nFold: 4 - val AUC: 0.5458   - loss: 324.071  - train AUC: 0.5507   - loss: 323.691 \nFold: 5 - val AUC: 0.5484   - loss: 323.882  - train AUC: 0.5514   - loss: 323.637 \nScore:  - val AUC: 0.5461   - loss: 32421.627 - train AUC: 0.5525   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.5741   - loss: 32421.627\nCum CV train: 0.5813   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_2', 'var_2_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5491   - loss: 323.750  - train AUC: 0.5560   - loss: 323.350 \nFold: 2 - val AUC: 0.5483   - loss: 323.822  - train AUC: 0.5585   - loss: 323.246 \nFold: 3 - val AUC: 0.5538   - loss: 323.375  - train AUC: 0.5591   - loss: 323.206 \nFold: 4 - val AUC: 0.5524   - loss: 323.372  - train AUC: 0.5596   - loss: 323.218 \nFold: 5 - val AUC: 0.5555   - loss: 323.287  - train AUC: 0.5581   - loss: 323.338 \nScore:  - val AUC: 0.5506   - loss: 32421.627 - train AUC: 0.5601   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.5968   - loss: 32421.627\nCum CV train: 0.6042   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_3', 'var_3_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5111   - loss: 326.001  - train AUC: 0.5196   - loss: 325.879 \nFold: 2 - val AUC: 0.5053   - loss: 326.037  - train AUC: 0.5196   - loss: 325.869 \nFold: 3 - val AUC: 0.5065   - loss: 326.112  - train AUC: 0.5198   - loss: 325.837 \nFold: 4 - val AUC: 0.5124   - loss: 326.073  - train AUC: 0.5220   - loss: 325.826 \nFold: 5 - val AUC: 0.5094   - loss: 326.097  - train AUC: 0.5193   - loss: 325.852 \nScore:  - val AUC: 0.5085   - loss: 32421.627 - train AUC: 0.5216   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.5981   - loss: 32421.627\nCum CV train: 0.6072   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_4', 'var_4_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5092   - loss: 325.992  - train AUC: 0.5230   - loss: 325.888 \nFold: 2 - val AUC: 0.4975   - loss: 326.300  - train AUC: 0.5208   - loss: 325.876 \nFold: 3 - val AUC: 0.4976   - loss: 326.186  - train AUC: 0.5234   - loss: 325.876 \nFold: 4 - val AUC: 0.5104   - loss: 326.016  - train AUC: 0.5227   - loss: 325.889 \nFold: 5 - val AUC: 0.5086   - loss: 326.006  - train AUC: 0.5243   - loss: 325.813 \nScore:  - val AUC: 0.5045   - loss: 32421.627 - train AUC: 0.5269   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.5986   - loss: 32421.627\nCum CV train: 0.6096   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_5', 'var_5_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5320   - loss: 324.690  - train AUC: 0.5372   - loss: 324.288 \nFold: 2 - val AUC: 0.5256   - loss: 324.578  - train AUC: 0.5343   - loss: 324.434 \nFold: 3 - val AUC: 0.5292   - loss: 324.617  - train AUC: 0.5340   - loss: 324.406 \nFold: 4 - val AUC: 0.5322   - loss: 324.151  - train AUC: 0.5350   - loss: 324.464 \nFold: 5 - val AUC: 0.5261   - loss: 324.859  - train AUC: 0.5343   - loss: 324.346 \nScore:  - val AUC: 0.5289   - loss: 32421.627 - train AUC: 0.5362   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6079   - loss: 32421.627\nCum CV train: 0.6193   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_6', 'var_6_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5597   - loss: 323.316  - train AUC: 0.5650   - loss: 323.070 \nFold: 2 - val AUC: 0.5552   - loss: 323.309  - train AUC: 0.5679   - loss: 322.938 \nFold: 3 - val AUC: 0.5554   - loss: 323.503  - train AUC: 0.5650   - loss: 323.015 \nFold: 4 - val AUC: 0.5591   - loss: 323.079  - train AUC: 0.5647   - loss: 323.084 \nFold: 5 - val AUC: 0.5643   - loss: 322.995  - train AUC: 0.5671   - loss: 322.988 \nScore:  - val AUC: 0.5579   - loss: 32421.627 - train AUC: 0.5691   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6282   - loss: 32421.627\nCum CV train: 0.6386   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_7', 'var_7_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5071   - loss: 326.093  - train AUC: 0.5185   - loss: 326.008 \nFold: 2 - val AUC: 0.5058   - loss: 326.104  - train AUC: 0.5176   - loss: 326.028 \nFold: 3 - val AUC: 0.5038   - loss: 326.259  - train AUC: 0.5198   - loss: 326.003 \nFold: 4 - val AUC: 0.5034   - loss: 326.202  - train AUC: 0.5213   - loss: 325.961 \nFold: 5 - val AUC: 0.4927   - loss: 326.327  - train AUC: 0.5191   - loss: 325.999 \nScore:  - val AUC: 0.5023   - loss: 32421.627 - train AUC: 0.5216   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6279   - loss: 32421.627\nCum CV train: 0.6397   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_8', 'var_8_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5142   - loss: 325.797  - train AUC: 0.5306   - loss: 325.612 \nFold: 2 - val AUC: 0.5164   - loss: 325.900  - train AUC: 0.5290   - loss: 325.578 \nFold: 3 - val AUC: 0.5125   - loss: 325.865  - train AUC: 0.5283   - loss: 325.660 \nFold: 4 - val AUC: 0.5221   - loss: 325.765  - train AUC: 0.5264   - loss: 325.657 \nFold: 5 - val AUC: 0.5197   - loss: 325.804  - train AUC: 0.5261   - loss: 325.650 \nScore:  - val AUC: 0.5167   - loss: 32421.627 - train AUC: 0.5306   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6299   - loss: 32421.627\nCum CV train: 0.6427   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_9', 'var_9_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5396   - loss: 324.742  - train AUC: 0.5479   - loss: 324.219 \nFold: 2 - val AUC: 0.5419   - loss: 324.274  - train AUC: 0.5482   - loss: 324.272 \nFold: 3 - val AUC: 0.5399   - loss: 324.329  - train AUC: 0.5515   - loss: 324.114 \nFold: 4 - val AUC: 0.5430   - loss: 324.631  - train AUC: 0.5474   - loss: 324.188 \nFold: 5 - val AUC: 0.5468   - loss: 324.349  - train AUC: 0.5476   - loss: 324.252 \nScore:  - val AUC: 0.5422   - loss: 32421.627 - train AUC: 0.5503   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6404   - loss: 32421.627\nCum CV train: 0.6536   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_10', 'var_10_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5061   - loss: 326.077  - train AUC: 0.5206   - loss: 325.991 \nFold: 2 - val AUC: 0.5051   - loss: 326.063  - train AUC: 0.5216   - loss: 325.972 \nFold: 3 - val AUC: 0.4897   - loss: 326.274  - train AUC: 0.5204   - loss: 326.015 \nFold: 4 - val AUC: 0.5094   - loss: 326.093  - train AUC: 0.5209   - loss: 325.969 \nFold: 5 - val AUC: 0.5012   - loss: 326.204  - train AUC: 0.5171   - loss: 325.985 \nScore:  - val AUC: 0.5022   - loss: 32421.627 - train AUC: 0.5232   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6406   - loss: 32421.627\nCum CV train: 0.6547   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_11', 'var_11_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5206   - loss: 325.642  - train AUC: 0.5267   - loss: 325.473 \nFold: 2 - val AUC: 0.5212   - loss: 325.789  - train AUC: 0.5270   - loss: 325.472 \nFold: 3 - val AUC: 0.5178   - loss: 325.692  - train AUC: 0.5275   - loss: 325.510 \nFold: 4 - val AUC: 0.5154   - loss: 325.621  - train AUC: 0.5313   - loss: 325.368 \nFold: 5 - val AUC: 0.5176   - loss: 325.754  - train AUC: 0.5292   - loss: 325.431 \nScore:  - val AUC: 0.5183   - loss: 32421.627 - train AUC: 0.5300   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6431   - loss: 32421.627\nCum CV train: 0.6583   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_12', 'var_12_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5522   - loss: 323.238  - train AUC: 0.5657   - loss: 322.312 \nFold: 2 - val AUC: 0.5546   - loss: 323.442  - train AUC: 0.5656   - loss: 322.265 \nFold: 3 - val AUC: 0.5587   - loss: 322.137  - train AUC: 0.5657   - loss: 322.562 \nFold: 4 - val AUC: 0.5667   - loss: 322.097  - train AUC: 0.5629   - loss: 322.598 \nFold: 5 - val AUC: 0.5577   - loss: 322.556  - train AUC: 0.5638   - loss: 322.476 \nScore:  - val AUC: 0.5575   - loss: 32421.627 - train AUC: 0.5672   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6599   - loss: 32421.627\nCum CV train: 0.6742   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_13', 'var_13_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5507   - loss: 323.804  - train AUC: 0.5601   - loss: 323.088 \nFold: 2 - val AUC: 0.5515   - loss: 323.735  - train AUC: 0.5593   - loss: 323.201 \nFold: 3 - val AUC: 0.5536   - loss: 323.635  - train AUC: 0.5579   - loss: 323.246 \nFold: 4 - val AUC: 0.5578   - loss: 322.971  - train AUC: 0.5575   - loss: 323.372 \nFold: 5 - val AUC: 0.5610   - loss: 322.982  - train AUC: 0.5581   - loss: 323.309 \nScore:  - val AUC: 0.5545   - loss: 32421.627 - train AUC: 0.5594   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6711   - loss: 32421.627\nCum CV train: 0.6852   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_14', 'var_14_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.4985   - loss: 326.189  - train AUC: 0.5220   - loss: 325.977 \nFold: 2 - val AUC: 0.5117   - loss: 326.098  - train AUC: 0.5205   - loss: 325.947 \nFold: 3 - val AUC: 0.5020   - loss: 326.220  - train AUC: 0.5199   - loss: 325.976 \nFold: 4 - val AUC: 0.5067   - loss: 326.162  - train AUC: 0.5233   - loss: 325.937 \nFold: 5 - val AUC: 0.5079   - loss: 326.204  - train AUC: 0.5219   - loss: 325.930 \nScore:  - val AUC: 0.5049   - loss: 32421.627 - train AUC: 0.5237   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6712   - loss: 32421.627\nCum CV train: 0.6863   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_15', 'var_15_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5196   - loss: 325.878  - train AUC: 0.5221   - loss: 325.817 \nFold: 2 - val AUC: 0.5049   - loss: 326.166  - train AUC: 0.5257   - loss: 325.682 \nFold: 3 - val AUC: 0.5207   - loss: 325.848  - train AUC: 0.5249   - loss: 325.741 \nFold: 4 - val AUC: 0.5106   - loss: 326.003  - train AUC: 0.5247   - loss: 325.759 \nFold: 5 - val AUC: 0.5149   - loss: 325.906  - train AUC: 0.5228   - loss: 325.782 \nScore:  - val AUC: 0.5136   - loss: 32421.627 - train AUC: 0.5265   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6722   - loss: 32421.627\nCum CV train: 0.6881   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_16', 'var_16_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5083   - loss: 326.140  - train AUC: 0.5218   - loss: 325.868 \nFold: 2 - val AUC: 0.5099   - loss: 326.050  - train AUC: 0.5208   - loss: 325.903 \nFold: 3 - val AUC: 0.5123   - loss: 326.014  - train AUC: 0.5213   - loss: 325.942 \nFold: 4 - val AUC: 0.5124   - loss: 326.037  - train AUC: 0.5257   - loss: 325.840 \nFold: 5 - val AUC: 0.5033   - loss: 326.221  - train AUC: 0.5234   - loss: 325.854 \nScore:  - val AUC: 0.5086   - loss: 32421.627 - train AUC: 0.5267   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6723   - loss: 32421.627\nCum CV train: 0.6892   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_17', 'var_17_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.4936   - loss: 326.223  - train AUC: 0.5190   - loss: 326.066 \nFold: 2 - val AUC: 0.4982   - loss: 326.171  - train AUC: 0.5199   - loss: 326.042 \nFold: 3 - val AUC: 0.4949   - loss: 326.307  - train AUC: 0.5182   - loss: 326.038 \nFold: 4 - val AUC: 0.4994   - loss: 326.212  - train AUC: 0.5199   - loss: 326.048 \nFold: 5 - val AUC: 0.4931   - loss: 326.270  - train AUC: 0.5205   - loss: 326.017 \nScore:  - val AUC: 0.4952   - loss: 32421.627 - train AUC: 0.5243   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6720   - loss: 32421.627\nCum CV train: 0.6899   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_18', 'var_18_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5407   - loss: 324.065  - train AUC: 0.5432   - loss: 324.079 \nFold: 2 - val AUC: 0.5378   - loss: 324.292  - train AUC: 0.5442   - loss: 323.996 \nFold: 3 - val AUC: 0.5431   - loss: 324.182  - train AUC: 0.5452   - loss: 323.936 \nFold: 4 - val AUC: 0.5358   - loss: 324.308  - train AUC: 0.5428   - loss: 324.044 \nFold: 5 - val AUC: 0.5384   - loss: 324.225  - train AUC: 0.5449   - loss: 323.969 \nScore:  - val AUC: 0.5389   - loss: 32421.627 - train AUC: 0.5449   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6798   - loss: 32421.627\nCum CV train: 0.6977   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_19', 'var_19_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5125   - loss: 325.795  - train AUC: 0.5217   - loss: 325.772 \nFold: 2 - val AUC: 0.5056   - loss: 326.185  - train AUC: 0.5246   - loss: 325.624 \nFold: 3 - val AUC: 0.5109   - loss: 325.845  - train AUC: 0.5244   - loss: 325.698 \nFold: 4 - val AUC: 0.5127   - loss: 325.875  - train AUC: 0.5214   - loss: 325.761 \nFold: 5 - val AUC: 0.5148   - loss: 325.815  - train AUC: 0.5231   - loss: 325.722 \nScore:  - val AUC: 0.5104   - loss: 32421.627 - train AUC: 0.5247   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6809   - loss: 32421.627\nCum CV train: 0.6993   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_20', 'var_20_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5171   - loss: 325.798  - train AUC: 0.5281   - loss: 325.501 \nFold: 2 - val AUC: 0.5138   - loss: 325.840  - train AUC: 0.5285   - loss: 325.507 \nFold: 3 - val AUC: 0.5180   - loss: 325.655  - train AUC: 0.5289   - loss: 325.522 \nFold: 4 - val AUC: 0.5210   - loss: 325.687  - train AUC: 0.5314   - loss: 325.450 \nFold: 5 - val AUC: 0.5230   - loss: 325.613  - train AUC: 0.5264   - loss: 325.573 \nScore:  - val AUC: 0.5184   - loss: 32421.627 - train AUC: 0.5301   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6828   - loss: 32421.627\nCum CV train: 0.7018   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_21', 'var_21_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5681   - loss: 322.632  - train AUC: 0.5615   - loss: 323.135 \nFold: 2 - val AUC: 0.5496   - loss: 324.233  - train AUC: 0.5639   - loss: 322.865 \nFold: 3 - val AUC: 0.5463   - loss: 323.966  - train AUC: 0.5638   - loss: 323.014 \nFold: 4 - val AUC: 0.5618   - loss: 323.395  - train AUC: 0.5582   - loss: 323.272 \nFold: 5 - val AUC: 0.5601   - loss: 322.678  - train AUC: 0.5632   - loss: 323.141 \nScore:  - val AUC: 0.5566   - loss: 32421.627 - train AUC: 0.5635   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.6935   - loss: 32421.627\nCum CV train: 0.7120   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_22', 'var_22_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5528   - loss: 323.494  - train AUC: 0.5584   - loss: 322.942 \nFold: 2 - val AUC: 0.5540   - loss: 322.888  - train AUC: 0.5564   - loss: 323.178 \nFold: 3 - val AUC: 0.5468   - loss: 323.616  - train AUC: 0.5595   - loss: 322.945 \nFold: 4 - val AUC: 0.5495   - loss: 323.598  - train AUC: 0.5578   - loss: 322.984 \nFold: 5 - val AUC: 0.5585   - loss: 322.803  - train AUC: 0.5590   - loss: 323.060 \nScore:  - val AUC: 0.5522   - loss: 32421.627 - train AUC: 0.5596   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7030   - loss: 32421.627\nCum CV train: 0.7211   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_23', 'var_23_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5277   - loss: 325.562  - train AUC: 0.5293   - loss: 325.542 \nFold: 2 - val AUC: 0.5206   - loss: 325.792  - train AUC: 0.5278   - loss: 325.600 \nFold: 3 - val AUC: 0.5218   - loss: 325.679  - train AUC: 0.5282   - loss: 325.577 \nFold: 4 - val AUC: 0.5209   - loss: 325.875  - train AUC: 0.5283   - loss: 325.551 \nFold: 5 - val AUC: 0.5230   - loss: 325.890  - train AUC: 0.5292   - loss: 325.498 \nScore:  - val AUC: 0.5224   - loss: 32421.627 - train AUC: 0.5295   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7044   - loss: 32421.627\nCum CV train: 0.7230   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_24', 'var_24_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5301   - loss: 325.248  - train AUC: 0.5370   - loss: 325.225 \nFold: 2 - val AUC: 0.5303   - loss: 325.262  - train AUC: 0.5349   - loss: 325.334 \nFold: 3 - val AUC: 0.5407   - loss: 325.139  - train AUC: 0.5344   - loss: 325.257 \nFold: 4 - val AUC: 0.5162   - loss: 325.977  - train AUC: 0.5375   - loss: 325.154 \nFold: 5 - val AUC: 0.5240   - loss: 325.803  - train AUC: 0.5367   - loss: 325.152 \nScore:  - val AUC: 0.5279   - loss: 32421.627 - train AUC: 0.5375   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7065   - loss: 32421.627\nCum CV train: 0.7257   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_25', 'var_25_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5124   - loss: 325.985  - train AUC: 0.5221   - loss: 325.906 \nFold: 2 - val AUC: 0.5071   - loss: 326.141  - train AUC: 0.5209   - loss: 325.911 \nFold: 3 - val AUC: 0.5064   - loss: 326.199  - train AUC: 0.5213   - loss: 325.850 \nFold: 4 - val AUC: 0.5118   - loss: 326.028  - train AUC: 0.5221   - loss: 325.872 \nFold: 5 - val AUC: 0.5100   - loss: 326.112  - train AUC: 0.5232   - loss: 325.862 \nScore:  - val AUC: 0.5093   - loss: 32421.627 - train AUC: 0.5239   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7067   - loss: 32421.627\nCum CV train: 0.7265   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_26', 'var_26_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5564   - loss: 322.833  - train AUC: 0.5634   - loss: 322.074 \nFold: 2 - val AUC: 0.5574   - loss: 322.307  - train AUC: 0.5616   - loss: 322.279 \nFold: 3 - val AUC: 0.5535   - loss: 322.287  - train AUC: 0.5635   - loss: 322.302 \nFold: 4 - val AUC: 0.5596   - loss: 322.878  - train AUC: 0.5631   - loss: 322.055 \nFold: 5 - val AUC: 0.5662   - loss: 321.841  - train AUC: 0.5627   - loss: 322.243 \nScore:  - val AUC: 0.5581   - loss: 32421.627 - train AUC: 0.5638   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7175   - loss: 32421.627\nCum CV train: 0.7366   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_27', 'var_27_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5013   - loss: 326.142  - train AUC: 0.5192   - loss: 326.024 \nFold: 2 - val AUC: 0.4934   - loss: 326.244  - train AUC: 0.5182   - loss: 326.038 \nFold: 3 - val AUC: 0.4920   - loss: 326.256  - train AUC: 0.5185   - loss: 326.047 \nFold: 4 - val AUC: 0.5002   - loss: 326.240  - train AUC: 0.5194   - loss: 325.986 \nFold: 5 - val AUC: 0.4884   - loss: 326.337  - train AUC: 0.5207   - loss: 325.969 \nScore:  - val AUC: 0.4950   - loss: 32421.627 - train AUC: 0.5229   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7173   - loss: 32421.627\nCum CV train: 0.7371   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_28', 'var_28_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5167   - loss: 325.948  - train AUC: 0.5315   - loss: 325.603 \nFold: 2 - val AUC: 0.5232   - loss: 325.789  - train AUC: 0.5305   - loss: 325.596 \nFold: 3 - val AUC: 0.5229   - loss: 325.707  - train AUC: 0.5307   - loss: 325.592 \nFold: 4 - val AUC: 0.5239   - loss: 325.797  - train AUC: 0.5307   - loss: 325.590 \nFold: 5 - val AUC: 0.5257   - loss: 325.734  - train AUC: 0.5329   - loss: 325.527 \nScore:  - val AUC: 0.5219   - loss: 32421.627 - train AUC: 0.5332   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7186   - loss: 32421.627\nCum CV train: 0.7389   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_29', 'var_29_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5019   - loss: 326.140  - train AUC: 0.5216   - loss: 326.011 \nFold: 2 - val AUC: 0.4999   - loss: 326.144  - train AUC: 0.5184   - loss: 326.038 \nFold: 3 - val AUC: 0.4892   - loss: 326.300  - train AUC: 0.5170   - loss: 326.027 \nFold: 4 - val AUC: 0.4937   - loss: 326.302  - train AUC: 0.5186   - loss: 326.010 \nFold: 5 - val AUC: 0.4954   - loss: 326.251  - train AUC: 0.5176   - loss: 326.022 \nScore:  - val AUC: 0.4956   - loss: 32421.627 - train AUC: 0.5229   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7184   - loss: 32421.627\nCum CV train: 0.7394   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_30', 'var_30_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5030   - loss: 326.153  - train AUC: 0.5183   - loss: 326.024 \nFold: 2 - val AUC: 0.4963   - loss: 326.214  - train AUC: 0.5197   - loss: 326.038 \nFold: 3 - val AUC: 0.5036   - loss: 326.193  - train AUC: 0.5213   - loss: 326.001 \nFold: 4 - val AUC: 0.4921   - loss: 326.382  - train AUC: 0.5197   - loss: 325.987 \nFold: 5 - val AUC: 0.4989   - loss: 326.204  - train AUC: 0.5195   - loss: 326.065 \nScore:  - val AUC: 0.4988   - loss: 32421.627 - train AUC: 0.5244   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7182   - loss: 32421.627\nCum CV train: 0.7398   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_31', 'var_31_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5254   - loss: 325.734  - train AUC: 0.5343   - loss: 325.557 \nFold: 2 - val AUC: 0.5286   - loss: 325.663  - train AUC: 0.5324   - loss: 325.605 \nFold: 3 - val AUC: 0.5169   - loss: 326.059  - train AUC: 0.5347   - loss: 325.533 \nFold: 4 - val AUC: 0.5294   - loss: 325.715  - train AUC: 0.5313   - loss: 325.620 \nFold: 5 - val AUC: 0.5294   - loss: 325.733  - train AUC: 0.5337   - loss: 325.539 \nScore:  - val AUC: 0.5256   - loss: 32421.627 - train AUC: 0.5349   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7194   - loss: 32421.627\nCum CV train: 0.7414   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_32', 'var_32_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5269   - loss: 325.277  - train AUC: 0.5337   - loss: 325.059 \nFold: 2 - val AUC: 0.5245   - loss: 325.290  - train AUC: 0.5335   - loss: 325.095 \nFold: 3 - val AUC: 0.5262   - loss: 325.203  - train AUC: 0.5329   - loss: 325.114 \nFold: 4 - val AUC: 0.5290   - loss: 325.213  - train AUC: 0.5338   - loss: 325.093 \nFold: 5 - val AUC: 0.5307   - loss: 325.322  - train AUC: 0.5337   - loss: 325.051 \nScore:  - val AUC: 0.5266   - loss: 32421.627 - train AUC: 0.5351   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7224   - loss: 32421.627\nCum CV train: 0.7446   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_33', 'var_33_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5328   - loss: 324.241  - train AUC: 0.5453   - loss: 323.813 \nFold: 2 - val AUC: 0.5466   - loss: 323.569  - train AUC: 0.5442   - loss: 323.872 \nFold: 3 - val AUC: 0.5444   - loss: 323.586  - train AUC: 0.5453   - loss: 323.919 \nFold: 4 - val AUC: 0.5317   - loss: 324.655  - train AUC: 0.5453   - loss: 323.714 \nFold: 5 - val AUC: 0.5400   - loss: 324.299  - train AUC: 0.5468   - loss: 323.681 \nScore:  - val AUC: 0.5391   - loss: 32421.627 - train AUC: 0.5471   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7283   - loss: 32421.627\nCum CV train: 0.7502   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_34', 'var_34_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5476   - loss: 324.114  - train AUC: 0.5498   - loss: 324.251 \nFold: 2 - val AUC: 0.5462   - loss: 324.228  - train AUC: 0.5513   - loss: 324.193 \nFold: 3 - val AUC: 0.5480   - loss: 324.231  - train AUC: 0.5498   - loss: 324.209 \nFold: 4 - val AUC: 0.5419   - loss: 324.594  - train AUC: 0.5516   - loss: 324.097 \nFold: 5 - val AUC: 0.5452   - loss: 324.730  - train AUC: 0.5495   - loss: 324.086 \nScore:  - val AUC: 0.5452   - loss: 32421.627 - train AUC: 0.5512   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7333   - loss: 32421.627\nCum CV train: 0.7551   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_35', 'var_35_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5319   - loss: 325.198  - train AUC: 0.5449   - loss: 324.789 \nFold: 2 - val AUC: 0.5319   - loss: 325.290  - train AUC: 0.5424   - loss: 324.895 \nFold: 3 - val AUC: 0.5431   - loss: 324.656  - train AUC: 0.5417   - loss: 325.002 \nFold: 4 - val AUC: 0.5366   - loss: 325.173  - train AUC: 0.5426   - loss: 324.846 \nFold: 5 - val AUC: 0.5335   - loss: 325.287  - train AUC: 0.5421   - loss: 324.890 \nScore:  - val AUC: 0.5351   - loss: 32421.627 - train AUC: 0.5444   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7365   - loss: 32421.627\nCum CV train: 0.7585   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_36', 'var_36_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5354   - loss: 325.388  - train AUC: 0.5437   - loss: 324.781 \nFold: 2 - val AUC: 0.5430   - loss: 324.728  - train AUC: 0.5417   - loss: 324.954 \nFold: 3 - val AUC: 0.5335   - loss: 325.305  - train AUC: 0.5446   - loss: 324.784 \nFold: 4 - val AUC: 0.5347   - loss: 325.216  - train AUC: 0.5444   - loss: 324.825 \nFold: 5 - val AUC: 0.5411   - loss: 324.883  - train AUC: 0.5439   - loss: 324.862 \nScore:  - val AUC: 0.5369   - loss: 32421.627 - train AUC: 0.5450   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7395   - loss: 32421.627\nCum CV train: 0.7618   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_37', 'var_37_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5099   - loss: 326.005  - train AUC: 0.5202   - loss: 325.891 \nFold: 2 - val AUC: 0.5019   - loss: 326.093  - train AUC: 0.5213   - loss: 325.887 \nFold: 3 - val AUC: 0.5039   - loss: 326.165  - train AUC: 0.5187   - loss: 325.914 \nFold: 4 - val AUC: 0.5053   - loss: 326.078  - train AUC: 0.5213   - loss: 325.875 \nFold: 5 - val AUC: 0.5011   - loss: 326.139  - train AUC: 0.5225   - loss: 325.875 \nScore:  - val AUC: 0.5044   - loss: 32421.627 - train AUC: 0.5252   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7397   - loss: 32421.627\nCum CV train: 0.7626   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_38', 'var_38_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5011   - loss: 326.175  - train AUC: 0.5223   - loss: 326.022 \nFold: 2 - val AUC: 0.5048   - loss: 326.138  - train AUC: 0.5225   - loss: 325.979 \nFold: 3 - val AUC: 0.5069   - loss: 326.164  - train AUC: 0.5216   - loss: 325.975 \nFold: 4 - val AUC: 0.4993   - loss: 326.236  - train AUC: 0.5219   - loss: 326.004 \nFold: 5 - val AUC: 0.5037   - loss: 326.200  - train AUC: 0.5220   - loss: 326.004 \nScore:  - val AUC: 0.5031   - loss: 32421.627 - train AUC: 0.5249   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7397   - loss: 32421.627\nCum CV train: 0.7630   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_39', 'var_39_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.4985   - loss: 326.209  - train AUC: 0.5193   - loss: 325.961 \nFold: 2 - val AUC: 0.5084   - loss: 326.027  - train AUC: 0.5228   - loss: 325.965 \nFold: 3 - val AUC: 0.5076   - loss: 326.164  - train AUC: 0.5179   - loss: 326.003 \nFold: 4 - val AUC: 0.5059   - loss: 326.183  - train AUC: 0.5229   - loss: 325.923 \nFold: 5 - val AUC: 0.4941   - loss: 326.250  - train AUC: 0.5218   - loss: 325.943 \nScore:  - val AUC: 0.5025   - loss: 32421.627 - train AUC: 0.5247   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7396   - loss: 32421.627\nCum CV train: 0.7635   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_40', 'var_40_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5465   - loss: 323.427  - train AUC: 0.5502   - loss: 323.550 \nFold: 2 - val AUC: 0.5426   - loss: 323.772  - train AUC: 0.5505   - loss: 323.485 \nFold: 3 - val AUC: 0.5465   - loss: 323.736  - train AUC: 0.5518   - loss: 323.401 \nFold: 4 - val AUC: 0.5506   - loss: 323.690  - train AUC: 0.5498   - loss: 323.468 \nFold: 5 - val AUC: 0.5420   - loss: 323.738  - train AUC: 0.5498   - loss: 323.493 \nScore:  - val AUC: 0.5452   - loss: 32421.627 - train AUC: 0.5516   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7457   - loss: 32421.627\nCum CV train: 0.7691   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_41', 'var_41_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5037   - loss: 326.115  - train AUC: 0.5184   - loss: 326.037 \nFold: 2 - val AUC: 0.5027   - loss: 326.127  - train AUC: 0.5207   - loss: 326.041 \nFold: 3 - val AUC: 0.5035   - loss: 326.210  - train AUC: 0.5193   - loss: 326.001 \nFold: 4 - val AUC: 0.5036   - loss: 326.244  - train AUC: 0.5189   - loss: 326.000 \nFold: 5 - val AUC: 0.5013   - loss: 326.220  - train AUC: 0.5196   - loss: 326.000 \nScore:  - val AUC: 0.5029   - loss: 32421.627 - train AUC: 0.5224   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7456   - loss: 32421.627\nCum CV train: 0.7695   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_42', 'var_42_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5024   - loss: 326.177  - train AUC: 0.5232   - loss: 325.911 \nFold: 2 - val AUC: 0.5038   - loss: 326.191  - train AUC: 0.5228   - loss: 325.945 \nFold: 3 - val AUC: 0.5102   - loss: 326.141  - train AUC: 0.5195   - loss: 325.997 \nFold: 4 - val AUC: 0.5022   - loss: 326.217  - train AUC: 0.5230   - loss: 325.950 \nFold: 5 - val AUC: 0.5034   - loss: 326.192  - train AUC: 0.5217   - loss: 325.956 \nScore:  - val AUC: 0.5033   - loss: 32421.627 - train AUC: 0.5244   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7456   - loss: 32421.627\nCum CV train: 0.7701   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_43', 'var_43_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5281   - loss: 325.491  - train AUC: 0.5327   - loss: 325.379 \nFold: 2 - val AUC: 0.5204   - loss: 325.734  - train AUC: 0.5353   - loss: 325.284 \nFold: 3 - val AUC: 0.5240   - loss: 325.480  - train AUC: 0.5339   - loss: 325.366 \nFold: 4 - val AUC: 0.5224   - loss: 325.721  - train AUC: 0.5332   - loss: 325.333 \nFold: 5 - val AUC: 0.5336   - loss: 325.316  - train AUC: 0.5352   - loss: 325.314 \nScore:  - val AUC: 0.5252   - loss: 32421.627 - train AUC: 0.5356   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7471   - loss: 32421.627\nCum CV train: 0.7718   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_44', 'var_44_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5394   - loss: 323.830  - train AUC: 0.5513   - loss: 323.089 \nFold: 2 - val AUC: 0.5397   - loss: 323.426  - train AUC: 0.5493   - loss: 323.294 \nFold: 3 - val AUC: 0.5461   - loss: 323.620  - train AUC: 0.5487   - loss: 323.174 \nFold: 4 - val AUC: 0.5474   - loss: 323.049  - train AUC: 0.5509   - loss: 323.267 \nFold: 5 - val AUC: 0.5474   - loss: 323.043  - train AUC: 0.5510   - loss: 323.284 \nScore:  - val AUC: 0.5441   - loss: 32421.627 - train AUC: 0.5524   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7540   - loss: 32421.627\nCum CV train: 0.7780   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_45', 'var_45_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5201   - loss: 325.508  - train AUC: 0.5295   - loss: 325.476 \nFold: 2 - val AUC: 0.5200   - loss: 325.571  - train AUC: 0.5305   - loss: 325.467 \nFold: 3 - val AUC: 0.5217   - loss: 325.846  - train AUC: 0.5271   - loss: 325.470 \nFold: 4 - val AUC: 0.5223   - loss: 325.570  - train AUC: 0.5257   - loss: 325.625 \nFold: 5 - val AUC: 0.5100   - loss: 326.006  - train AUC: 0.5287   - loss: 325.506 \nScore:  - val AUC: 0.5187   - loss: 32421.627 - train AUC: 0.5310   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7553   - loss: 32421.627\nCum CV train: 0.7796   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_46', 'var_46_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.4991   - loss: 326.102  - train AUC: 0.5181   - loss: 325.997 \nFold: 2 - val AUC: 0.5064   - loss: 326.123  - train AUC: 0.5183   - loss: 325.974 \nFold: 3 - val AUC: 0.5024   - loss: 326.149  - train AUC: 0.5229   - loss: 325.872 \nFold: 4 - val AUC: 0.4975   - loss: 326.211  - train AUC: 0.5202   - loss: 325.949 \nFold: 5 - val AUC: 0.5067   - loss: 326.097  - train AUC: 0.5199   - loss: 325.960 \nScore:  - val AUC: 0.5018   - loss: 32421.627 - train AUC: 0.5259   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7554   - loss: 32421.627\nCum CV train: 0.7801   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_47', 'var_47_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5020   - loss: 326.214  - train AUC: 0.5204   - loss: 325.935 \nFold: 2 - val AUC: 0.4989   - loss: 326.210  - train AUC: 0.5212   - loss: 325.942 \nFold: 3 - val AUC: 0.5101   - loss: 326.045  - train AUC: 0.5210   - loss: 325.950 \nFold: 4 - val AUC: 0.5015   - loss: 326.236  - train AUC: 0.5236   - loss: 325.892 \nFold: 5 - val AUC: 0.5060   - loss: 326.128  - train AUC: 0.5200   - loss: 325.979 \nScore:  - val AUC: 0.5027   - loss: 32421.627 - train AUC: 0.5244   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7553   - loss: 32421.627\nCum CV train: 0.7805   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_48', 'var_48_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5302   - loss: 325.330  - train AUC: 0.5385   - loss: 325.156 \nFold: 2 - val AUC: 0.5232   - loss: 325.553  - train AUC: 0.5405   - loss: 325.075 \nFold: 3 - val AUC: 0.5281   - loss: 325.564  - train AUC: 0.5394   - loss: 325.039 \nFold: 4 - val AUC: 0.5400   - loss: 324.958  - train AUC: 0.5381   - loss: 325.163 \nFold: 5 - val AUC: 0.5406   - loss: 325.089  - train AUC: 0.5378   - loss: 325.131 \nScore:  - val AUC: 0.5322   - loss: 32421.627 - train AUC: 0.5394   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7575   - loss: 32421.627\nCum CV train: 0.7828   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_49', 'var_49_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5314   - loss: 324.792  - train AUC: 0.5333   - loss: 324.815 \nFold: 2 - val AUC: 0.5261   - loss: 325.218  - train AUC: 0.5342   - loss: 324.740 \nFold: 3 - val AUC: 0.5288   - loss: 325.106  - train AUC: 0.5346   - loss: 324.701 \nFold: 4 - val AUC: 0.5252   - loss: 324.923  - train AUC: 0.5366   - loss: 324.747 \nFold: 5 - val AUC: 0.5275   - loss: 324.895  - train AUC: 0.5345   - loss: 324.769 \nScore:  - val AUC: 0.5274   - loss: 32421.627 - train AUC: 0.5364   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7601   - loss: 32421.627\nCum CV train: 0.7855   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_50', 'var_50_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5158   - loss: 325.960  - train AUC: 0.5253   - loss: 325.806 \nFold: 2 - val AUC: 0.5210   - loss: 325.841  - train AUC: 0.5239   - loss: 325.846 \nFold: 3 - val AUC: 0.5099   - loss: 326.122  - train AUC: 0.5265   - loss: 325.812 \nFold: 4 - val AUC: 0.5111   - loss: 326.039  - train AUC: 0.5261   - loss: 325.778 \nFold: 5 - val AUC: 0.5078   - loss: 326.163  - train AUC: 0.5243   - loss: 325.824 \nScore:  - val AUC: 0.5124   - loss: 32421.627 - train AUC: 0.5277   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7604   - loss: 32421.627\nCum CV train: 0.7863   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_51', 'var_51_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5277   - loss: 324.736  - train AUC: 0.5297   - loss: 325.159 \nFold: 2 - val AUC: 0.5221   - loss: 325.171  - train AUC: 0.5334   - loss: 325.007 \nFold: 3 - val AUC: 0.5245   - loss: 325.079  - train AUC: 0.5347   - loss: 324.917 \nFold: 4 - val AUC: 0.5200   - loss: 325.617  - train AUC: 0.5334   - loss: 324.903 \nFold: 5 - val AUC: 0.5241   - loss: 325.380  - train AUC: 0.5299   - loss: 325.009 \nScore:  - val AUC: 0.5234   - loss: 32421.627 - train AUC: 0.5345   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7626   - loss: 32421.627\nCum CV train: 0.7885   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_52', 'var_52_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5287   - loss: 325.421  - train AUC: 0.5338   - loss: 325.342 \nFold: 2 - val AUC: 0.5332   - loss: 325.340  - train AUC: 0.5351   - loss: 325.266 \nFold: 3 - val AUC: 0.5249   - loss: 325.545  - train AUC: 0.5384   - loss: 325.115 \nFold: 4 - val AUC: 0.5266   - loss: 325.539  - train AUC: 0.5362   - loss: 325.212 \nFold: 5 - val AUC: 0.5312   - loss: 325.342  - train AUC: 0.5369   - loss: 325.189 \nScore:  - val AUC: 0.5285   - loss: 32421.627 - train AUC: 0.5379   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7646   - loss: 32421.627\nCum CV train: 0.7906   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_53', 'var_53_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5606   - loss: 323.294  - train AUC: 0.5591   - loss: 322.805 \nFold: 2 - val AUC: 0.5523   - loss: 323.224  - train AUC: 0.5630   - loss: 322.737 \nFold: 3 - val AUC: 0.5516   - loss: 323.515  - train AUC: 0.5628   - loss: 322.671 \nFold: 4 - val AUC: 0.5543   - loss: 322.989  - train AUC: 0.5607   - loss: 322.902 \nFold: 5 - val AUC: 0.5606   - loss: 322.458  - train AUC: 0.5603   - loss: 322.918 \nScore:  - val AUC: 0.5554   - loss: 32421.627 - train AUC: 0.5621   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7715   - loss: 32421.627\nCum CV train: 0.7969   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_54', 'var_54_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5204   - loss: 325.837  - train AUC: 0.5268   - loss: 325.744 \nFold: 2 - val AUC: 0.5212   - loss: 325.828  - train AUC: 0.5269   - loss: 325.757 \nFold: 3 - val AUC: 0.5207   - loss: 325.940  - train AUC: 0.5264   - loss: 325.694 \nFold: 4 - val AUC: 0.5183   - loss: 325.875  - train AUC: 0.5273   - loss: 325.689 \nFold: 5 - val AUC: 0.5155   - loss: 325.981  - train AUC: 0.5256   - loss: 325.730 \nScore:  - val AUC: 0.5189   - loss: 32421.627 - train AUC: 0.5280   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7721   - loss: 32421.627\nCum CV train: 0.7978   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_55', 'var_55_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5074   - loss: 325.853  - train AUC: 0.5256   - loss: 325.634 \nFold: 2 - val AUC: 0.5203   - loss: 325.750  - train AUC: 0.5218   - loss: 325.689 \nFold: 3 - val AUC: 0.5100   - loss: 325.943  - train AUC: 0.5248   - loss: 325.603 \nFold: 4 - val AUC: 0.5053   - loss: 326.021  - train AUC: 0.5257   - loss: 325.589 \nFold: 5 - val AUC: 0.5177   - loss: 325.728  - train AUC: 0.5225   - loss: 325.669 \nScore:  - val AUC: 0.5113   - loss: 32421.627 - train AUC: 0.5262   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7728   - loss: 32421.627\nCum CV train: 0.7988   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_56', 'var_56_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5291   - loss: 324.944  - train AUC: 0.5325   - loss: 324.711 \nFold: 2 - val AUC: 0.5253   - loss: 325.182  - train AUC: 0.5353   - loss: 324.640 \nFold: 3 - val AUC: 0.5300   - loss: 325.096  - train AUC: 0.5323   - loss: 324.673 \nFold: 4 - val AUC: 0.5300   - loss: 324.885  - train AUC: 0.5336   - loss: 324.713 \nFold: 5 - val AUC: 0.5316   - loss: 324.370  - train AUC: 0.5352   - loss: 324.810 \nScore:  - val AUC: 0.5287   - loss: 32421.627 - train AUC: 0.5348   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7751   - loss: 32421.627\nCum CV train: 0.8011   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_57', 'var_57_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5100   - loss: 326.042  - train AUC: 0.5247   - loss: 325.855 \nFold: 2 - val AUC: 0.5105   - loss: 326.078  - train AUC: 0.5228   - loss: 325.891 \nFold: 3 - val AUC: 0.5195   - loss: 325.983  - train AUC: 0.5250   - loss: 325.795 \nFold: 4 - val AUC: 0.5108   - loss: 326.069  - train AUC: 0.5243   - loss: 325.813 \nFold: 5 - val AUC: 0.5107   - loss: 326.144  - train AUC: 0.5224   - loss: 325.875 \nScore:  - val AUC: 0.5116   - loss: 32421.627 - train AUC: 0.5261   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7753   - loss: 32421.627\nCum CV train: 0.8017   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_58', 'var_58_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5245   - loss: 325.809  - train AUC: 0.5322   - loss: 325.498 \nFold: 2 - val AUC: 0.5318   - loss: 325.400  - train AUC: 0.5331   - loss: 325.529 \nFold: 3 - val AUC: 0.5310   - loss: 325.537  - train AUC: 0.5331   - loss: 325.443 \nFold: 4 - val AUC: 0.5316   - loss: 325.547  - train AUC: 0.5326   - loss: 325.446 \nFold: 5 - val AUC: 0.5159   - loss: 326.020  - train AUC: 0.5347   - loss: 325.436 \nScore:  - val AUC: 0.5265   - loss: 32421.627 - train AUC: 0.5340   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7764   - loss: 32421.627\nCum CV train: 0.8031   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_59', 'var_59_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5060   - loss: 326.058  - train AUC: 0.5255   - loss: 325.760 \nFold: 2 - val AUC: 0.5064   - loss: 325.984  - train AUC: 0.5236   - loss: 325.836 \nFold: 3 - val AUC: 0.5034   - loss: 326.112  - train AUC: 0.5235   - loss: 325.812 \nFold: 4 - val AUC: 0.5044   - loss: 326.148  - train AUC: 0.5243   - loss: 325.750 \nFold: 5 - val AUC: 0.5126   - loss: 326.021  - train AUC: 0.5293   - loss: 325.684 \nScore:  - val AUC: 0.5063   - loss: 32421.627 - train AUC: 0.5299   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7767   - loss: 32421.627\nCum CV train: 0.8038   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_60', 'var_60_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5106   - loss: 326.026  - train AUC: 0.5205   - loss: 325.864 \nFold: 2 - val AUC: 0.5024   - loss: 326.060  - train AUC: 0.5216   - loss: 325.848 \nFold: 3 - val AUC: 0.5050   - loss: 326.105  - train AUC: 0.5194   - loss: 325.879 \nFold: 4 - val AUC: 0.5034   - loss: 326.087  - train AUC: 0.5205   - loss: 325.902 \nFold: 5 - val AUC: 0.5152   - loss: 326.102  - train AUC: 0.5198   - loss: 325.901 \nScore:  - val AUC: 0.5075   - loss: 32421.627 - train AUC: 0.5227   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7769   - loss: 32421.627\nCum CV train: 0.8044   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_61', 'var_61_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5039   - loss: 326.071  - train AUC: 0.5220   - loss: 325.910 \nFold: 2 - val AUC: 0.5052   - loss: 326.078  - train AUC: 0.5177   - loss: 325.929 \nFold: 3 - val AUC: 0.5146   - loss: 325.994  - train AUC: 0.5222   - loss: 325.919 \nFold: 4 - val AUC: 0.5003   - loss: 326.170  - train AUC: 0.5191   - loss: 325.902 \nFold: 5 - val AUC: 0.5010   - loss: 326.189  - train AUC: 0.5214   - loss: 325.882 \nScore:  - val AUC: 0.5047   - loss: 32421.627 - train AUC: 0.5242   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7770   - loss: 32421.627\nCum CV train: 0.8049   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_62', 'var_62_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5055   - loss: 326.033  - train AUC: 0.5237   - loss: 325.854 \nFold: 2 - val AUC: 0.5151   - loss: 325.915  - train AUC: 0.5222   - loss: 325.857 \nFold: 3 - val AUC: 0.5166   - loss: 325.972  - train AUC: 0.5238   - loss: 325.764 \nFold: 4 - val AUC: 0.5099   - loss: 326.029  - train AUC: 0.5221   - loss: 325.820 \nFold: 5 - val AUC: 0.5080   - loss: 326.143  - train AUC: 0.5237   - loss: 325.780 \nScore:  - val AUC: 0.5104   - loss: 32421.627 - train AUC: 0.5260   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7773   - loss: 32421.627\nCum CV train: 0.8055   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_63', 'var_63_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5161   - loss: 325.804  - train AUC: 0.5228   - loss: 325.634 \nFold: 2 - val AUC: 0.5003   - loss: 326.102  - train AUC: 0.5269   - loss: 325.523 \nFold: 3 - val AUC: 0.5140   - loss: 325.868  - train AUC: 0.5239   - loss: 325.644 \nFold: 4 - val AUC: 0.5110   - loss: 325.796  - train AUC: 0.5282   - loss: 325.633 \nFold: 5 - val AUC: 0.5084   - loss: 325.856  - train AUC: 0.5249   - loss: 325.646 \nScore:  - val AUC: 0.5093   - loss: 32421.627 - train AUC: 0.5285   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7781   - loss: 32421.627\nCum CV train: 0.8066   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_64', 'var_64_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5188   - loss: 325.879  - train AUC: 0.5216   - loss: 325.853 \nFold: 2 - val AUC: 0.5112   - loss: 326.184  - train AUC: 0.5211   - loss: 325.824 \nFold: 3 - val AUC: 0.5108   - loss: 326.019  - train AUC: 0.5232   - loss: 325.810 \nFold: 4 - val AUC: 0.5135   - loss: 325.958  - train AUC: 0.5239   - loss: 325.807 \nFold: 5 - val AUC: 0.5006   - loss: 326.321  - train AUC: 0.5233   - loss: 325.763 \nScore:  - val AUC: 0.5103   - loss: 32421.627 - train AUC: 0.5244   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7783   - loss: 32421.627\nCum CV train: 0.8073   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_65', 'var_65_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5116   - loss: 326.035  - train AUC: 0.5217   - loss: 325.895 \nFold: 2 - val AUC: 0.5106   - loss: 326.058  - train AUC: 0.5210   - loss: 325.925 \nFold: 3 - val AUC: 0.5109   - loss: 326.133  - train AUC: 0.5205   - loss: 325.893 \nFold: 4 - val AUC: 0.5078   - loss: 326.147  - train AUC: 0.5203   - loss: 325.950 \nFold: 5 - val AUC: 0.5057   - loss: 326.186  - train AUC: 0.5215   - loss: 325.902 \nScore:  - val AUC: 0.5090   - loss: 32421.627 - train AUC: 0.5227   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7785   - loss: 32421.627\nCum CV train: 0.8078   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_66', 'var_66_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5161   - loss: 325.923  - train AUC: 0.5317   - loss: 325.497 \nFold: 2 - val AUC: 0.5228   - loss: 325.666  - train AUC: 0.5277   - loss: 325.649 \nFold: 3 - val AUC: 0.5238   - loss: 325.748  - train AUC: 0.5279   - loss: 325.629 \nFold: 4 - val AUC: 0.5185   - loss: 325.889  - train AUC: 0.5319   - loss: 325.515 \nFold: 5 - val AUC: 0.5217   - loss: 325.872  - train AUC: 0.5267   - loss: 325.626 \nScore:  - val AUC: 0.5204   - loss: 32421.627 - train AUC: 0.5310   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7793   - loss: 32421.627\nCum CV train: 0.8089   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_67', 'var_67_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5397   - loss: 324.529  - train AUC: 0.5494   - loss: 324.458 \nFold: 2 - val AUC: 0.5501   - loss: 324.387  - train AUC: 0.5488   - loss: 324.375 \nFold: 3 - val AUC: 0.5472   - loss: 324.553  - train AUC: 0.5466   - loss: 324.521 \nFold: 4 - val AUC: 0.5495   - loss: 324.564  - train AUC: 0.5473   - loss: 324.440 \nFold: 5 - val AUC: 0.5385   - loss: 325.105  - train AUC: 0.5502   - loss: 324.277 \nScore:  - val AUC: 0.5444   - loss: 32421.627 - train AUC: 0.5490   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7823   - loss: 32421.627\nCum CV train: 0.8118   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_68', 'var_68_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5123   - loss: 325.983  - train AUC: 0.5188   - loss: 325.924 \nFold: 2 - val AUC: 0.5090   - loss: 326.101  - train AUC: 0.5209   - loss: 325.924 \nFold: 3 - val AUC: 0.5053   - loss: 326.197  - train AUC: 0.5211   - loss: 325.873 \nFold: 4 - val AUC: 0.5134   - loss: 326.032  - train AUC: 0.5219   - loss: 325.870 \nFold: 5 - val AUC: 0.5069   - loss: 326.098  - train AUC: 0.5225   - loss: 325.878 \nScore:  - val AUC: 0.5092   - loss: 32421.627 - train AUC: 0.5237   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7826   - loss: 32421.627\nCum CV train: 0.8124   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_69', 'var_69_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5006   - loss: 326.109  - train AUC: 0.5195   - loss: 325.752 \nFold: 2 - val AUC: 0.5070   - loss: 325.927  - train AUC: 0.5196   - loss: 325.788 \nFold: 3 - val AUC: 0.5069   - loss: 325.968  - train AUC: 0.5181   - loss: 325.826 \nFold: 4 - val AUC: 0.5069   - loss: 325.935  - train AUC: 0.5209   - loss: 325.813 \nFold: 5 - val AUC: 0.5114   - loss: 325.850  - train AUC: 0.5197   - loss: 325.799 \nScore:  - val AUC: 0.5064   - loss: 32421.627 - train AUC: 0.5205   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7830   - loss: 32421.627\nCum CV train: 0.8131   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_70', 'var_70_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5240   - loss: 325.418  - train AUC: 0.5334   - loss: 325.172 \nFold: 2 - val AUC: 0.5248   - loss: 325.321  - train AUC: 0.5336   - loss: 325.200 \nFold: 3 - val AUC: 0.5269   - loss: 325.395  - train AUC: 0.5339   - loss: 325.140 \nFold: 4 - val AUC: 0.5314   - loss: 325.072  - train AUC: 0.5347   - loss: 325.121 \nFold: 5 - val AUC: 0.5210   - loss: 325.519  - train AUC: 0.5350   - loss: 325.069 \nScore:  - val AUC: 0.5257   - loss: 32421.627 - train AUC: 0.5359   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7848   - loss: 32421.627\nCum CV train: 0.8149   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_71', 'var_71_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5366   - loss: 325.340  - train AUC: 0.5356   - loss: 325.326 \nFold: 2 - val AUC: 0.5293   - loss: 325.579  - train AUC: 0.5374   - loss: 325.295 \nFold: 3 - val AUC: 0.5258   - loss: 325.643  - train AUC: 0.5386   - loss: 325.238 \nFold: 4 - val AUC: 0.5300   - loss: 325.655  - train AUC: 0.5368   - loss: 325.240 \nFold: 5 - val AUC: 0.5342   - loss: 325.334  - train AUC: 0.5363   - loss: 325.311 \nScore:  - val AUC: 0.5309   - loss: 32421.627 - train AUC: 0.5378   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7860   - loss: 32421.627\nCum CV train: 0.8162   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_72', 'var_72_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5120   - loss: 326.019  - train AUC: 0.5203   - loss: 325.839 \nFold: 2 - val AUC: 0.5037   - loss: 326.123  - train AUC: 0.5214   - loss: 325.819 \nFold: 3 - val AUC: 0.5153   - loss: 325.913  - train AUC: 0.5197   - loss: 325.836 \nFold: 4 - val AUC: 0.5144   - loss: 325.863  - train AUC: 0.5237   - loss: 325.803 \nFold: 5 - val AUC: 0.5109   - loss: 326.082  - train AUC: 0.5215   - loss: 325.806 \nScore:  - val AUC: 0.5106   - loss: 32421.627 - train AUC: 0.5231   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7862   - loss: 32421.627\nCum CV train: 0.8168   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_73', 'var_73_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5028   - loss: 326.088  - train AUC: 0.5210   - loss: 325.936 \nFold: 2 - val AUC: 0.4973   - loss: 326.162  - train AUC: 0.5219   - loss: 325.935 \nFold: 3 - val AUC: 0.5044   - loss: 326.126  - train AUC: 0.5218   - loss: 325.881 \nFold: 4 - val AUC: 0.4966   - loss: 326.214  - train AUC: 0.5199   - loss: 325.925 \nFold: 5 - val AUC: 0.5129   - loss: 326.009  - train AUC: 0.5212   - loss: 325.957 \nScore:  - val AUC: 0.5023   - loss: 32421.627 - train AUC: 0.5245   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7863   - loss: 32421.627\nCum CV train: 0.8172   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_74', 'var_74_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5121   - loss: 325.995  - train AUC: 0.5281   - loss: 325.630 \nFold: 2 - val AUC: 0.5311   - loss: 325.679  - train AUC: 0.5275   - loss: 325.625 \nFold: 3 - val AUC: 0.5240   - loss: 325.769  - train AUC: 0.5287   - loss: 325.590 \nFold: 4 - val AUC: 0.5226   - loss: 325.788  - train AUC: 0.5277   - loss: 325.668 \nFold: 5 - val AUC: 0.5191   - loss: 325.897  - train AUC: 0.5297   - loss: 325.569 \nScore:  - val AUC: 0.5211   - loss: 32421.627 - train AUC: 0.5294   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7871   - loss: 32421.627\nCum CV train: 0.8182   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_75', 'var_75_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5350   - loss: 324.355  - train AUC: 0.5422   - loss: 324.261 \nFold: 2 - val AUC: 0.5450   - loss: 324.326  - train AUC: 0.5420   - loss: 324.191 \nFold: 3 - val AUC: 0.5380   - loss: 324.434  - train AUC: 0.5427   - loss: 324.214 \nFold: 4 - val AUC: 0.5401   - loss: 324.478  - train AUC: 0.5416   - loss: 324.248 \nFold: 5 - val AUC: 0.5341   - loss: 324.506  - train AUC: 0.5455   - loss: 324.139 \nScore:  - val AUC: 0.5380   - loss: 32421.627 - train AUC: 0.5439   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7902   - loss: 32421.627\nCum CV train: 0.8210   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_76', 'var_76_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5673   - loss: 322.728  - train AUC: 0.5621   - loss: 322.748 \nFold: 2 - val AUC: 0.5660   - loss: 322.174  - train AUC: 0.5642   - loss: 322.749 \nFold: 3 - val AUC: 0.5581   - loss: 323.332  - train AUC: 0.5661   - loss: 322.444 \nFold: 4 - val AUC: 0.5585   - loss: 322.779  - train AUC: 0.5661   - loss: 322.568 \nFold: 5 - val AUC: 0.5582   - loss: 323.157  - train AUC: 0.5658   - loss: 322.452 \nScore:  - val AUC: 0.5611   - loss: 32421.627 - train AUC: 0.5653   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7960   - loss: 32421.627\nCum CV train: 0.8258   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_77', 'var_77_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5114   - loss: 325.888  - train AUC: 0.5262   - loss: 325.722 \nFold: 2 - val AUC: 0.5150   - loss: 325.879  - train AUC: 0.5251   - loss: 325.720 \nFold: 3 - val AUC: 0.5147   - loss: 325.963  - train AUC: 0.5310   - loss: 325.560 \nFold: 4 - val AUC: 0.5199   - loss: 325.887  - train AUC: 0.5264   - loss: 325.717 \nFold: 5 - val AUC: 0.5188   - loss: 325.853  - train AUC: 0.5270   - loss: 325.665 \nScore:  - val AUC: 0.5156   - loss: 32421.627 - train AUC: 0.5290   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.7965   - loss: 32421.627\nCum CV train: 0.8266   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_78', 'var_78_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5453   - loss: 323.450  - train AUC: 0.5513   - loss: 323.735 \nFold: 2 - val AUC: 0.5489   - loss: 323.377  - train AUC: 0.5475   - loss: 323.821 \nFold: 3 - val AUC: 0.5395   - loss: 324.327  - train AUC: 0.5496   - loss: 323.583 \nFold: 4 - val AUC: 0.5388   - loss: 324.145  - train AUC: 0.5474   - loss: 323.664 \nFold: 5 - val AUC: 0.5368   - loss: 324.365  - train AUC: 0.5477   - loss: 323.683 \nScore:  - val AUC: 0.5420   - loss: 32421.627 - train AUC: 0.5500   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8005   - loss: 32421.627\nCum CV train: 0.8301   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_79', 'var_79_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5034   - loss: 326.074  - train AUC: 0.5235   - loss: 325.935 \nFold: 2 - val AUC: 0.5027   - loss: 326.122  - train AUC: 0.5204   - loss: 325.983 \nFold: 3 - val AUC: 0.4956   - loss: 326.203  - train AUC: 0.5221   - loss: 325.938 \nFold: 4 - val AUC: 0.5064   - loss: 326.131  - train AUC: 0.5230   - loss: 325.924 \nFold: 5 - val AUC: 0.4979   - loss: 326.167  - train AUC: 0.5208   - loss: 325.950 \nScore:  - val AUC: 0.5010   - loss: 32421.627 - train AUC: 0.5270   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8005   - loss: 32421.627\nCum CV train: 0.8304   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_80', 'var_80_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5545   - loss: 322.845  - train AUC: 0.5636   - loss: 322.525 \nFold: 2 - val AUC: 0.5552   - loss: 323.469  - train AUC: 0.5620   - loss: 322.527 \nFold: 3 - val AUC: 0.5562   - loss: 322.891  - train AUC: 0.5640   - loss: 322.411 \nFold: 4 - val AUC: 0.5685   - loss: 321.996  - train AUC: 0.5623   - loss: 322.585 \nFold: 5 - val AUC: 0.5568   - loss: 322.630  - train AUC: 0.5631   - loss: 322.551 \nScore:  - val AUC: 0.5572   - loss: 32421.627 - train AUC: 0.5643   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8061   - loss: 32421.627\nCum CV train: 0.8352   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_81', 'var_81_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5705   - loss: 321.209  - train AUC: 0.5778   - loss: 320.779 \nFold: 2 - val AUC: 0.5736   - loss: 320.382  - train AUC: 0.5802   - loss: 320.824 \nFold: 3 - val AUC: 0.5709   - loss: 321.167  - train AUC: 0.5801   - loss: 320.664 \nFold: 4 - val AUC: 0.5863   - loss: 320.306  - train AUC: 0.5761   - loss: 320.878 \nFold: 5 - val AUC: 0.5686   - loss: 321.811  - train AUC: 0.5797   - loss: 320.538 \nScore:  - val AUC: 0.5734   - loss: 32421.627 - train AUC: 0.5800   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8144   - loss: 32421.627\nCum CV train: 0.8421   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_82', 'var_82_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5200   - loss: 325.541  - train AUC: 0.5300   - loss: 325.178 \nFold: 2 - val AUC: 0.5211   - loss: 325.303  - train AUC: 0.5293   - loss: 325.213 \nFold: 3 - val AUC: 0.5208   - loss: 325.164  - train AUC: 0.5318   - loss: 325.181 \nFold: 4 - val AUC: 0.5192   - loss: 325.424  - train AUC: 0.5314   - loss: 325.149 \nFold: 5 - val AUC: 0.5189   - loss: 325.607  - train AUC: 0.5302   - loss: 325.140 \nScore:  - val AUC: 0.5200   - loss: 32421.627 - train AUC: 0.5322   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8159   - loss: 32421.627\nCum CV train: 0.8437   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_83', 'var_83_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5199   - loss: 325.203  - train AUC: 0.5275   - loss: 325.268 \nFold: 2 - val AUC: 0.5077   - loss: 325.684  - train AUC: 0.5305   - loss: 325.131 \nFold: 3 - val AUC: 0.5183   - loss: 325.626  - train AUC: 0.5252   - loss: 325.225 \nFold: 4 - val AUC: 0.5136   - loss: 325.446  - train AUC: 0.5281   - loss: 325.213 \nFold: 5 - val AUC: 0.5279   - loss: 325.217  - train AUC: 0.5272   - loss: 325.226 \nScore:  - val AUC: 0.5166   - loss: 32421.627 - train AUC: 0.5294   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8169   - loss: 32421.627\nCum CV train: 0.8449   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_84', 'var_84_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5117   - loss: 326.085  - train AUC: 0.5218   - loss: 325.873 \nFold: 2 - val AUC: 0.5042   - loss: 326.082  - train AUC: 0.5241   - loss: 325.860 \nFold: 3 - val AUC: 0.5123   - loss: 326.072  - train AUC: 0.5206   - loss: 325.907 \nFold: 4 - val AUC: 0.5065   - loss: 326.139  - train AUC: 0.5233   - loss: 325.880 \nFold: 5 - val AUC: 0.5101   - loss: 326.074  - train AUC: 0.5216   - loss: 325.901 \nScore:  - val AUC: 0.5087   - loss: 32421.627 - train AUC: 0.5250   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8171   - loss: 32421.627\nCum CV train: 0.8453   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_85', 'var_85_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5249   - loss: 325.635  - train AUC: 0.5270   - loss: 325.402 \nFold: 2 - val AUC: 0.5251   - loss: 325.610  - train AUC: 0.5287   - loss: 325.370 \nFold: 3 - val AUC: 0.5287   - loss: 325.353  - train AUC: 0.5289   - loss: 325.396 \nFold: 4 - val AUC: 0.5104   - loss: 325.990  - train AUC: 0.5312   - loss: 325.381 \nFold: 5 - val AUC: 0.5195   - loss: 325.391  - train AUC: 0.5322   - loss: 325.353 \nScore:  - val AUC: 0.5210   - loss: 32421.627 - train AUC: 0.5319   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8180   - loss: 32421.627\nCum CV train: 0.8463   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_86', 'var_86_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5415   - loss: 324.173  - train AUC: 0.5431   - loss: 323.959 \nFold: 2 - val AUC: 0.5366   - loss: 324.180  - train AUC: 0.5459   - loss: 323.889 \nFold: 3 - val AUC: 0.5411   - loss: 323.917  - train AUC: 0.5432   - loss: 323.983 \nFold: 4 - val AUC: 0.5404   - loss: 324.028  - train AUC: 0.5412   - loss: 324.034 \nFold: 5 - val AUC: 0.5347   - loss: 324.491  - train AUC: 0.5432   - loss: 323.916 \nScore:  - val AUC: 0.5382   - loss: 32421.627 - train AUC: 0.5446   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8210   - loss: 32421.627\nCum CV train: 0.8490   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_87', 'var_87_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5391   - loss: 324.989  - train AUC: 0.5400   - loss: 324.908 \nFold: 2 - val AUC: 0.5369   - loss: 324.971  - train AUC: 0.5419   - loss: 324.848 \nFold: 3 - val AUC: 0.5432   - loss: 324.791  - train AUC: 0.5399   - loss: 324.965 \nFold: 4 - val AUC: 0.5237   - loss: 325.689  - train AUC: 0.5427   - loss: 324.863 \nFold: 5 - val AUC: 0.5348   - loss: 325.208  - train AUC: 0.5409   - loss: 324.861 \nScore:  - val AUC: 0.5349   - loss: 32421.627 - train AUC: 0.5418   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8224   - loss: 32421.627\nCum CV train: 0.8504   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_88', 'var_88_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5141   - loss: 325.858  - train AUC: 0.5271   - loss: 325.527 \nFold: 2 - val AUC: 0.5180   - loss: 325.794  - train AUC: 0.5285   - loss: 325.498 \nFold: 3 - val AUC: 0.5214   - loss: 325.551  - train AUC: 0.5273   - loss: 325.565 \nFold: 4 - val AUC: 0.5234   - loss: 325.583  - train AUC: 0.5248   - loss: 325.593 \nFold: 5 - val AUC: 0.5167   - loss: 325.808  - train AUC: 0.5262   - loss: 325.541 \nScore:  - val AUC: 0.5182   - loss: 32421.627 - train AUC: 0.5281   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8230   - loss: 32421.627\nCum CV train: 0.8510   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_89', 'var_89_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5327   - loss: 324.939  - train AUC: 0.5398   - loss: 324.730 \nFold: 2 - val AUC: 0.5273   - loss: 325.288  - train AUC: 0.5417   - loss: 324.668 \nFold: 3 - val AUC: 0.5349   - loss: 324.748  - train AUC: 0.5386   - loss: 324.792 \nFold: 4 - val AUC: 0.5382   - loss: 324.975  - train AUC: 0.5379   - loss: 324.711 \nFold: 5 - val AUC: 0.5344   - loss: 324.933  - train AUC: 0.5391   - loss: 324.747 \nScore:  - val AUC: 0.5329   - loss: 32421.627 - train AUC: 0.5403   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8245   - loss: 32421.627\nCum CV train: 0.8526   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_90', 'var_90_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5277   - loss: 325.198  - train AUC: 0.5337   - loss: 325.142 \nFold: 2 - val AUC: 0.5220   - loss: 325.445  - train AUC: 0.5327   - loss: 325.180 \nFold: 3 - val AUC: 0.5300   - loss: 325.384  - train AUC: 0.5340   - loss: 325.066 \nFold: 4 - val AUC: 0.5259   - loss: 325.436  - train AUC: 0.5324   - loss: 325.169 \nFold: 5 - val AUC: 0.5337   - loss: 325.075  - train AUC: 0.5333   - loss: 325.101 \nScore:  - val AUC: 0.5275   - loss: 32421.627 - train AUC: 0.5343   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8258   - loss: 32421.627\nCum CV train: 0.8538   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_91', 'var_91_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5366   - loss: 324.963  - train AUC: 0.5424   - loss: 324.753 \nFold: 2 - val AUC: 0.5472   - loss: 324.223  - train AUC: 0.5406   - loss: 324.921 \nFold: 3 - val AUC: 0.5340   - loss: 325.255  - train AUC: 0.5416   - loss: 324.678 \nFold: 4 - val AUC: 0.5288   - loss: 325.267  - train AUC: 0.5438   - loss: 324.637 \nFold: 5 - val AUC: 0.5326   - loss: 325.371  - train AUC: 0.5406   - loss: 324.742 \nScore:  - val AUC: 0.5352   - loss: 32421.627 - train AUC: 0.5431   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8275   - loss: 32421.627\nCum CV train: 0.8555   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_92', 'var_92_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5447   - loss: 323.706  - train AUC: 0.5482   - loss: 323.845 \nFold: 2 - val AUC: 0.5518   - loss: 323.464  - train AUC: 0.5477   - loss: 323.915 \nFold: 3 - val AUC: 0.5421   - loss: 324.487  - train AUC: 0.5462   - loss: 323.804 \nFold: 4 - val AUC: 0.5405   - loss: 324.204  - train AUC: 0.5489   - loss: 323.774 \nFold: 5 - val AUC: 0.5336   - loss: 324.403  - train AUC: 0.5498   - loss: 323.802 \nScore:  - val AUC: 0.5421   - loss: 32421.627 - train AUC: 0.5492   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8306   - loss: 32421.627\nCum CV train: 0.8582   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_93', 'var_93_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5235   - loss: 325.345  - train AUC: 0.5391   - loss: 325.054 \nFold: 2 - val AUC: 0.5312   - loss: 325.150  - train AUC: 0.5396   - loss: 324.979 \nFold: 3 - val AUC: 0.5313   - loss: 325.128  - train AUC: 0.5405   - loss: 324.920 \nFold: 4 - val AUC: 0.5356   - loss: 325.213  - train AUC: 0.5370   - loss: 325.019 \nFold: 5 - val AUC: 0.5271   - loss: 325.362  - train AUC: 0.5384   - loss: 324.960 \nScore:  - val AUC: 0.5295   - loss: 32421.627 - train AUC: 0.5410   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8318   - loss: 32421.627\nCum CV train: 0.8594   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_94', 'var_94_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5508   - loss: 323.445  - train AUC: 0.5463   - loss: 324.058 \nFold: 2 - val AUC: 0.5464   - loss: 323.914  - train AUC: 0.5493   - loss: 323.818 \nFold: 3 - val AUC: 0.5403   - loss: 324.137  - train AUC: 0.5501   - loss: 323.844 \nFold: 4 - val AUC: 0.5404   - loss: 324.198  - train AUC: 0.5501   - loss: 323.814 \nFold: 5 - val AUC: 0.5379   - loss: 324.794  - train AUC: 0.5483   - loss: 323.745 \nScore:  - val AUC: 0.5428   - loss: 32421.627 - train AUC: 0.5499   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8344   - loss: 32421.627\nCum CV train: 0.8617   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_95', 'var_95_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5352   - loss: 324.963  - train AUC: 0.5407   - loss: 324.851 \nFold: 2 - val AUC: 0.5417   - loss: 324.883  - train AUC: 0.5408   - loss: 324.821 \nFold: 3 - val AUC: 0.5315   - loss: 325.094  - train AUC: 0.5432   - loss: 324.772 \nFold: 4 - val AUC: 0.5328   - loss: 325.341  - train AUC: 0.5407   - loss: 324.787 \nFold: 5 - val AUC: 0.5381   - loss: 324.898  - train AUC: 0.5417   - loss: 324.858 \nScore:  - val AUC: 0.5357   - loss: 32421.627 - train AUC: 0.5426   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8359   - loss: 32421.627\nCum CV train: 0.8631   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_96', 'var_96_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.4963   - loss: 326.218  - train AUC: 0.5181   - loss: 326.007 \nFold: 2 - val AUC: 0.4981   - loss: 326.127  - train AUC: 0.5172   - loss: 326.046 \nFold: 3 - val AUC: 0.4994   - loss: 326.233  - train AUC: 0.5176   - loss: 326.045 \nFold: 4 - val AUC: 0.5032   - loss: 326.201  - train AUC: 0.5184   - loss: 326.019 \nFold: 5 - val AUC: 0.4931   - loss: 326.287  - train AUC: 0.5172   - loss: 326.010 \nScore:  - val AUC: 0.4977   - loss: 32421.627 - train AUC: 0.5220   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8359   - loss: 32421.627\nCum CV train: 0.8633   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_97', 'var_97_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5199   - loss: 325.773  - train AUC: 0.5269   - loss: 325.541 \nFold: 2 - val AUC: 0.5158   - loss: 325.657  - train AUC: 0.5245   - loss: 325.629 \nFold: 3 - val AUC: 0.5099   - loss: 325.979  - train AUC: 0.5241   - loss: 325.565 \nFold: 4 - val AUC: 0.5164   - loss: 325.708  - train AUC: 0.5274   - loss: 325.516 \nFold: 5 - val AUC: 0.5153   - loss: 325.682  - train AUC: 0.5264   - loss: 325.569 \nScore:  - val AUC: 0.5150   - loss: 32421.627 - train AUC: 0.5270   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8365   - loss: 32421.627\nCum CV train: 0.8641   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_98', 'var_98_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5040   - loss: 326.177  - train AUC: 0.5186   - loss: 326.055 \nFold: 2 - val AUC: 0.5062   - loss: 326.127  - train AUC: 0.5186   - loss: 326.017 \nFold: 3 - val AUC: 0.5001   - loss: 326.248  - train AUC: 0.5211   - loss: 325.957 \nFold: 4 - val AUC: 0.5024   - loss: 326.238  - train AUC: 0.5204   - loss: 325.988 \nFold: 5 - val AUC: 0.4941   - loss: 326.293  - train AUC: 0.5188   - loss: 325.996 \nScore:  - val AUC: 0.5011   - loss: 32421.627 - train AUC: 0.5230   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8364   - loss: 32421.627\nCum CV train: 0.8643   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_99', 'var_99_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5439   - loss: 324.375  - train AUC: 0.5591   - loss: 323.100 \nFold: 2 - val AUC: 0.5583   - loss: 323.433  - train AUC: 0.5558   - loss: 323.316 \nFold: 3 - val AUC: 0.5553   - loss: 323.622  - train AUC: 0.5557   - loss: 323.319 \nFold: 4 - val AUC: 0.5529   - loss: 322.933  - train AUC: 0.5590   - loss: 323.363 \nFold: 5 - val AUC: 0.5537   - loss: 323.328  - train AUC: 0.5572   - loss: 323.354 \nScore:  - val AUC: 0.5519   - loss: 32421.627 - train AUC: 0.5589   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8401   - loss: 32421.627\nCum CV train: 0.8673   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_100', 'var_100_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5088   - loss: 326.106  - train AUC: 0.5217   - loss: 325.978 \nFold: 2 - val AUC: 0.4945   - loss: 326.232  - train AUC: 0.5194   - loss: 326.008 \nFold: 3 - val AUC: 0.5038   - loss: 326.176  - train AUC: 0.5205   - loss: 325.961 \nFold: 4 - val AUC: 0.4955   - loss: 326.224  - train AUC: 0.5200   - loss: 325.996 \nFold: 5 - val AUC: 0.4936   - loss: 326.288  - train AUC: 0.5179   - loss: 326.019 \nScore:  - val AUC: 0.4991   - loss: 32421.627 - train AUC: 0.5232   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8400   - loss: 32421.627\nCum CV train: 0.8675   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_101', 'var_101_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5106   - loss: 325.941  - train AUC: 0.5224   - loss: 325.845 \nFold: 2 - val AUC: 0.5037   - loss: 326.152  - train AUC: 0.5216   - loss: 325.848 \nFold: 3 - val AUC: 0.5138   - loss: 325.958  - train AUC: 0.5225   - loss: 325.822 \nFold: 4 - val AUC: 0.5030   - loss: 326.129  - train AUC: 0.5242   - loss: 325.819 \nFold: 5 - val AUC: 0.5066   - loss: 326.160  - train AUC: 0.5240   - loss: 325.780 \nScore:  - val AUC: 0.5077   - loss: 32421.627 - train AUC: 0.5261   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8400   - loss: 32421.627\nCum CV train: 0.8678   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_102', 'var_102_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5183   - loss: 325.621  - train AUC: 0.5264   - loss: 325.373 \nFold: 2 - val AUC: 0.5259   - loss: 325.285  - train AUC: 0.5329   - loss: 325.265 \nFold: 3 - val AUC: 0.5219   - loss: 325.708  - train AUC: 0.5276   - loss: 325.293 \nFold: 4 - val AUC: 0.5186   - loss: 325.367  - train AUC: 0.5298   - loss: 325.337 \nFold: 5 - val AUC: 0.5158   - loss: 325.640  - train AUC: 0.5279   - loss: 325.315 \nScore:  - val AUC: 0.5199   - loss: 32421.627 - train AUC: 0.5308   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8408   - loss: 32421.627\nCum CV train: 0.8687   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_103', 'var_103_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.4962   - loss: 326.223  - train AUC: 0.5202   - loss: 326.002 \nFold: 2 - val AUC: 0.4964   - loss: 326.230  - train AUC: 0.5214   - loss: 326.037 \nFold: 3 - val AUC: 0.5022   - loss: 326.216  - train AUC: 0.5188   - loss: 325.993 \nFold: 4 - val AUC: 0.5020   - loss: 326.183  - train AUC: 0.5196   - loss: 325.993 \nFold: 5 - val AUC: 0.5031   - loss: 326.184  - train AUC: 0.5199   - loss: 325.984 \nScore:  - val AUC: 0.4996   - loss: 32421.627 - train AUC: 0.5229   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8407   - loss: 32421.627\nCum CV train: 0.8688   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_104', 'var_104_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5322   - loss: 325.566  - train AUC: 0.5321   - loss: 325.565 \nFold: 2 - val AUC: 0.5204   - loss: 325.960  - train AUC: 0.5343   - loss: 325.495 \nFold: 3 - val AUC: 0.5220   - loss: 325.867  - train AUC: 0.5341   - loss: 325.488 \nFold: 4 - val AUC: 0.5346   - loss: 325.506  - train AUC: 0.5307   - loss: 325.654 \nFold: 5 - val AUC: 0.5202   - loss: 325.973  - train AUC: 0.5340   - loss: 325.534 \nScore:  - val AUC: 0.5251   - loss: 32421.627 - train AUC: 0.5342   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8412   - loss: 32421.627\nCum CV train: 0.8695   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_105', 'var_105_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5240   - loss: 325.669  - train AUC: 0.5319   - loss: 325.511 \nFold: 2 - val AUC: 0.5235   - loss: 325.884  - train AUC: 0.5332   - loss: 325.477 \nFold: 3 - val AUC: 0.5217   - loss: 325.828  - train AUC: 0.5310   - loss: 325.551 \nFold: 4 - val AUC: 0.5210   - loss: 325.819  - train AUC: 0.5328   - loss: 325.502 \nFold: 5 - val AUC: 0.5253   - loss: 325.705  - train AUC: 0.5307   - loss: 325.559 \nScore:  - val AUC: 0.5225   - loss: 32421.627 - train AUC: 0.5331   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8417   - loss: 32421.627\nCum CV train: 0.8702   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_106', 'var_106_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5321   - loss: 325.104  - train AUC: 0.5366   - loss: 325.135 \nFold: 2 - val AUC: 0.5228   - loss: 325.524  - train AUC: 0.5369   - loss: 325.072 \nFold: 3 - val AUC: 0.5345   - loss: 325.178  - train AUC: 0.5377   - loss: 325.104 \nFold: 4 - val AUC: 0.5262   - loss: 325.461  - train AUC: 0.5396   - loss: 325.009 \nFold: 5 - val AUC: 0.5364   - loss: 325.309  - train AUC: 0.5341   - loss: 325.173 \nScore:  - val AUC: 0.5298   - loss: 32421.627 - train AUC: 0.5388   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8429   - loss: 32421.627\nCum CV train: 0.8713   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_107', 'var_107_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5348   - loss: 324.866  - train AUC: 0.5458   - loss: 324.382 \nFold: 2 - val AUC: 0.5337   - loss: 324.791  - train AUC: 0.5451   - loss: 324.356 \nFold: 3 - val AUC: 0.5395   - loss: 324.688  - train AUC: 0.5462   - loss: 324.315 \nFold: 4 - val AUC: 0.5435   - loss: 324.449  - train AUC: 0.5423   - loss: 324.479 \nFold: 5 - val AUC: 0.5437   - loss: 324.316  - train AUC: 0.5426   - loss: 324.534 \nScore:  - val AUC: 0.5384   - loss: 32421.627 - train AUC: 0.5449   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8448   - loss: 32421.627\nCum CV train: 0.8731   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_108', 'var_108_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5562   - loss: 323.936  - train AUC: 0.5501   - loss: 323.758 \nFold: 2 - val AUC: 0.5469   - loss: 323.842  - train AUC: 0.5504   - loss: 323.791 \nFold: 3 - val AUC: 0.5332   - loss: 324.694  - train AUC: 0.5527   - loss: 323.683 \nFold: 4 - val AUC: 0.5473   - loss: 323.946  - train AUC: 0.5523   - loss: 323.680 \nFold: 5 - val AUC: 0.5438   - loss: 323.647  - train AUC: 0.5536   - loss: 323.839 \nScore:  - val AUC: 0.5448   - loss: 32421.627 - train AUC: 0.5539   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8474   - loss: 32421.627\nCum CV train: 0.8754   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_109', 'var_109_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5416   - loss: 323.439  - train AUC: 0.5494   - loss: 322.715 \nFold: 2 - val AUC: 0.5424   - loss: 323.229  - train AUC: 0.5486   - loss: 322.773 \nFold: 3 - val AUC: 0.5486   - loss: 323.041  - train AUC: 0.5477   - loss: 322.847 \nFold: 4 - val AUC: 0.5370   - loss: 323.051  - train AUC: 0.5484   - loss: 322.859 \nFold: 5 - val AUC: 0.5454   - loss: 322.420  - train AUC: 0.5507   - loss: 322.924 \nScore:  - val AUC: 0.5427   - loss: 32421.627 - train AUC: 0.5496   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8507   - loss: 32421.627\nCum CV train: 0.8781   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_110', 'var_110_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5579   - loss: 322.305  - train AUC: 0.5620   - loss: 322.613 \nFold: 2 - val AUC: 0.5538   - loss: 323.692  - train AUC: 0.5628   - loss: 322.287 \nFold: 3 - val AUC: 0.5592   - loss: 322.213  - train AUC: 0.5637   - loss: 322.559 \nFold: 4 - val AUC: 0.5622   - loss: 322.295  - train AUC: 0.5615   - loss: 322.560 \nFold: 5 - val AUC: 0.5601   - loss: 322.884  - train AUC: 0.5629   - loss: 322.382 \nScore:  - val AUC: 0.5583   - loss: 32421.627 - train AUC: 0.5635   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8544   - loss: 32421.627\nCum CV train: 0.8811   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_111', 'var_111_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5211   - loss: 325.378  - train AUC: 0.5324   - loss: 325.362 \nFold: 2 - val AUC: 0.5238   - loss: 325.707  - train AUC: 0.5319   - loss: 325.278 \nFold: 3 - val AUC: 0.5184   - loss: 325.741  - train AUC: 0.5321   - loss: 325.321 \nFold: 4 - val AUC: 0.5258   - loss: 325.434  - train AUC: 0.5334   - loss: 325.293 \nFold: 5 - val AUC: 0.5245   - loss: 325.615  - train AUC: 0.5306   - loss: 325.330 \nScore:  - val AUC: 0.5225   - loss: 32421.627 - train AUC: 0.5331   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8551   - loss: 32421.627\nCum CV train: 0.8819   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_112', 'var_112_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5252   - loss: 325.449  - train AUC: 0.5360   - loss: 325.357 \nFold: 2 - val AUC: 0.5304   - loss: 325.511  - train AUC: 0.5343   - loss: 325.404 \nFold: 3 - val AUC: 0.5306   - loss: 325.459  - train AUC: 0.5335   - loss: 325.411 \nFold: 4 - val AUC: 0.5304   - loss: 325.632  - train AUC: 0.5343   - loss: 325.322 \nFold: 5 - val AUC: 0.5163   - loss: 325.906  - train AUC: 0.5369   - loss: 325.267 \nScore:  - val AUC: 0.5260   - loss: 32421.627 - train AUC: 0.5362   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8558   - loss: 32421.627\nCum CV train: 0.8827   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_113', 'var_113_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5124   - loss: 325.918  - train AUC: 0.5213   - loss: 325.775 \nFold: 2 - val AUC: 0.5118   - loss: 325.904  - train AUC: 0.5227   - loss: 325.669 \nFold: 3 - val AUC: 0.5190   - loss: 325.874  - train AUC: 0.5258   - loss: 325.617 \nFold: 4 - val AUC: 0.5116   - loss: 325.905  - train AUC: 0.5239   - loss: 325.704 \nFold: 5 - val AUC: 0.5093   - loss: 326.008  - train AUC: 0.5238   - loss: 325.721 \nScore:  - val AUC: 0.5126   - loss: 32421.627 - train AUC: 0.5254   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8561   - loss: 32421.627\nCum CV train: 0.8832   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_114', 'var_114_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5176   - loss: 325.692  - train AUC: 0.5319   - loss: 325.367 \nFold: 2 - val AUC: 0.5248   - loss: 325.643  - train AUC: 0.5333   - loss: 325.316 \nFold: 3 - val AUC: 0.5210   - loss: 325.615  - train AUC: 0.5302   - loss: 325.445 \nFold: 4 - val AUC: 0.5250   - loss: 325.478  - train AUC: 0.5305   - loss: 325.432 \nFold: 5 - val AUC: 0.5229   - loss: 325.561  - train AUC: 0.5306   - loss: 325.388 \nScore:  - val AUC: 0.5220   - loss: 32421.627 - train AUC: 0.5329   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8569   - loss: 32421.627\nCum CV train: 0.8840   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_115', 'var_115_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5400   - loss: 324.877  - train AUC: 0.5520   - loss: 324.181 \nFold: 2 - val AUC: 0.5422   - loss: 324.502  - train AUC: 0.5528   - loss: 324.188 \nFold: 3 - val AUC: 0.5486   - loss: 324.299  - train AUC: 0.5504   - loss: 324.299 \nFold: 4 - val AUC: 0.5499   - loss: 324.264  - train AUC: 0.5482   - loss: 324.381 \nFold: 5 - val AUC: 0.5463   - loss: 324.569  - train AUC: 0.5499   - loss: 324.281 \nScore:  - val AUC: 0.5443   - loss: 32421.627 - train AUC: 0.5522   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8586   - loss: 32421.627\nCum CV train: 0.8855   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_116', 'var_116_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5284   - loss: 325.641  - train AUC: 0.5316   - loss: 325.560 \nFold: 2 - val AUC: 0.5214   - loss: 325.759  - train AUC: 0.5318   - loss: 325.542 \nFold: 3 - val AUC: 0.5236   - loss: 325.789  - train AUC: 0.5318   - loss: 325.505 \nFold: 4 - val AUC: 0.5177   - loss: 325.904  - train AUC: 0.5335   - loss: 325.490 \nFold: 5 - val AUC: 0.5240   - loss: 325.686  - train AUC: 0.5326   - loss: 325.525 \nScore:  - val AUC: 0.5223   - loss: 32421.627 - train AUC: 0.5338   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8589   - loss: 32421.627\nCum CV train: 0.8860   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_117', 'var_117_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5036   - loss: 326.125  - train AUC: 0.5238   - loss: 325.939 \nFold: 2 - val AUC: 0.5090   - loss: 326.062  - train AUC: 0.5217   - loss: 325.951 \nFold: 3 - val AUC: 0.5092   - loss: 326.187  - train AUC: 0.5222   - loss: 325.915 \nFold: 4 - val AUC: 0.5016   - loss: 326.271  - train AUC: 0.5228   - loss: 325.932 \nFold: 5 - val AUC: 0.5099   - loss: 326.156  - train AUC: 0.5232   - loss: 325.934 \nScore:  - val AUC: 0.5066   - loss: 32421.627 - train AUC: 0.5255   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8590   - loss: 32421.627\nCum CV train: 0.8863   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_118', 'var_118_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5405   - loss: 324.737  - train AUC: 0.5463   - loss: 324.417 \nFold: 2 - val AUC: 0.5405   - loss: 324.380  - train AUC: 0.5456   - loss: 324.578 \nFold: 3 - val AUC: 0.5442   - loss: 324.461  - train AUC: 0.5489   - loss: 324.371 \nFold: 4 - val AUC: 0.5438   - loss: 324.713  - train AUC: 0.5457   - loss: 324.487 \nFold: 5 - val AUC: 0.5382   - loss: 325.233  - train AUC: 0.5450   - loss: 324.409 \nScore:  - val AUC: 0.5410   - loss: 32421.627 - train AUC: 0.5470   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8605   - loss: 32421.627\nCum CV train: 0.8877   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_119', 'var_119_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5360   - loss: 324.550  - train AUC: 0.5325   - loss: 324.828 \nFold: 2 - val AUC: 0.5271   - loss: 324.897  - train AUC: 0.5334   - loss: 324.767 \nFold: 3 - val AUC: 0.5285   - loss: 325.108  - train AUC: 0.5329   - loss: 324.739 \nFold: 4 - val AUC: 0.5274   - loss: 324.902  - train AUC: 0.5345   - loss: 324.723 \nFold: 5 - val AUC: 0.5222   - loss: 325.149  - train AUC: 0.5352   - loss: 324.709 \nScore:  - val AUC: 0.5277   - loss: 32421.627 - train AUC: 0.5343   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8618   - loss: 32421.627\nCum CV train: 0.8889   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_120', 'var_120_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5077   - loss: 326.078  - train AUC: 0.5247   - loss: 325.778 \nFold: 2 - val AUC: 0.5185   - loss: 325.884  - train AUC: 0.5240   - loss: 325.789 \nFold: 3 - val AUC: 0.5112   - loss: 326.074  - train AUC: 0.5253   - loss: 325.715 \nFold: 4 - val AUC: 0.5115   - loss: 326.080  - train AUC: 0.5226   - loss: 325.832 \nFold: 5 - val AUC: 0.5130   - loss: 325.985  - train AUC: 0.5221   - loss: 325.865 \nScore:  - val AUC: 0.5119   - loss: 32421.627 - train AUC: 0.5254   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8620   - loss: 32421.627\nCum CV train: 0.8892   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_121', 'var_121_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5302   - loss: 324.919  - train AUC: 0.5426   - loss: 324.507 \nFold: 2 - val AUC: 0.5338   - loss: 324.567  - train AUC: 0.5418   - loss: 324.587 \nFold: 3 - val AUC: 0.5402   - loss: 324.668  - train AUC: 0.5438   - loss: 324.473 \nFold: 4 - val AUC: 0.5375   - loss: 324.565  - train AUC: 0.5446   - loss: 324.442 \nFold: 5 - val AUC: 0.5348   - loss: 325.114  - train AUC: 0.5419   - loss: 324.488 \nScore:  - val AUC: 0.5348   - loss: 32421.627 - train AUC: 0.5446   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8638   - loss: 32421.627\nCum CV train: 0.8908   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_122', 'var_122_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5317   - loss: 324.815  - train AUC: 0.5450   - loss: 324.366 \nFold: 2 - val AUC: 0.5378   - loss: 324.757  - train AUC: 0.5446   - loss: 324.341 \nFold: 3 - val AUC: 0.5465   - loss: 324.110  - train AUC: 0.5430   - loss: 324.442 \nFold: 4 - val AUC: 0.5432   - loss: 324.504  - train AUC: 0.5431   - loss: 324.353 \nFold: 5 - val AUC: 0.5417   - loss: 324.528  - train AUC: 0.5445   - loss: 324.343 \nScore:  - val AUC: 0.5400   - loss: 32421.627 - train AUC: 0.5449   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8656   - loss: 32421.627\nCum CV train: 0.8924   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_123', 'var_123_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5447   - loss: 324.263  - train AUC: 0.5485   - loss: 323.851 \nFold: 2 - val AUC: 0.5384   - loss: 324.129  - train AUC: 0.5494   - loss: 323.902 \nFold: 3 - val AUC: 0.5436   - loss: 324.131  - train AUC: 0.5469   - loss: 323.985 \nFold: 4 - val AUC: 0.5466   - loss: 323.750  - train AUC: 0.5472   - loss: 323.985 \nFold: 5 - val AUC: 0.5401   - loss: 324.586  - train AUC: 0.5486   - loss: 323.785 \nScore:  - val AUC: 0.5423   - loss: 32421.627 - train AUC: 0.5489   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8673   - loss: 32421.627\nCum CV train: 0.8939   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_124', 'var_124_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5050   - loss: 326.105  - train AUC: 0.5192   - loss: 326.010 \nFold: 2 - val AUC: 0.5024   - loss: 326.128  - train AUC: 0.5220   - loss: 325.971 \nFold: 3 - val AUC: 0.5006   - loss: 326.233  - train AUC: 0.5204   - loss: 325.976 \nFold: 4 - val AUC: 0.5011   - loss: 326.259  - train AUC: 0.5203   - loss: 325.963 \nFold: 5 - val AUC: 0.5083   - loss: 326.170  - train AUC: 0.5193   - loss: 325.963 \nScore:  - val AUC: 0.5034   - loss: 32421.627 - train AUC: 0.5228   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8673   - loss: 32421.627\nCum CV train: 0.8941   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_125', 'var_125_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5278   - loss: 325.442  - train AUC: 0.5281   - loss: 325.477 \nFold: 2 - val AUC: 0.5166   - loss: 325.707  - train AUC: 0.5309   - loss: 325.378 \nFold: 3 - val AUC: 0.5243   - loss: 325.622  - train AUC: 0.5306   - loss: 325.358 \nFold: 4 - val AUC: 0.5262   - loss: 325.517  - train AUC: 0.5284   - loss: 325.419 \nFold: 5 - val AUC: 0.5153   - loss: 325.824  - train AUC: 0.5307   - loss: 325.380 \nScore:  - val AUC: 0.5214   - loss: 32421.627 - train AUC: 0.5318   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8679   - loss: 32421.627\nCum CV train: 0.8947   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_126', 'var_126_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5086   - loss: 326.126  - train AUC: 0.5208   - loss: 325.996 \nFold: 2 - val AUC: 0.5090   - loss: 326.078  - train AUC: 0.5230   - loss: 325.967 \nFold: 3 - val AUC: 0.5019   - loss: 326.203  - train AUC: 0.5204   - loss: 325.966 \nFold: 4 - val AUC: 0.4992   - loss: 326.286  - train AUC: 0.5208   - loss: 325.963 \nFold: 5 - val AUC: 0.5067   - loss: 326.213  - train AUC: 0.5212   - loss: 325.947 \nScore:  - val AUC: 0.5047   - loss: 32421.627 - train AUC: 0.5227   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8679   - loss: 32421.627\nCum CV train: 0.8949   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_127', 'var_127_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5437   - loss: 324.585  - train AUC: 0.5437   - loss: 324.593 \nFold: 2 - val AUC: 0.5502   - loss: 324.088  - train AUC: 0.5438   - loss: 324.588 \nFold: 3 - val AUC: 0.5331   - loss: 325.221  - train AUC: 0.5478   - loss: 324.352 \nFold: 4 - val AUC: 0.5355   - loss: 324.947  - train AUC: 0.5450   - loss: 324.555 \nFold: 5 - val AUC: 0.5330   - loss: 324.941  - train AUC: 0.5453   - loss: 324.515 \nScore:  - val AUC: 0.5386   - loss: 32421.627 - train AUC: 0.5466   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8696   - loss: 32421.627\nCum CV train: 0.8963   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_128', 'var_128_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5280   - loss: 325.338  - train AUC: 0.5285   - loss: 325.390 \nFold: 2 - val AUC: 0.5208   - loss: 325.619  - train AUC: 0.5293   - loss: 325.358 \nFold: 3 - val AUC: 0.5244   - loss: 325.518  - train AUC: 0.5296   - loss: 325.357 \nFold: 4 - val AUC: 0.5210   - loss: 325.719  - train AUC: 0.5336   - loss: 325.230 \nFold: 5 - val AUC: 0.5164   - loss: 325.690  - train AUC: 0.5309   - loss: 325.337 \nScore:  - val AUC: 0.5215   - loss: 32421.627 - train AUC: 0.5313   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8703   - loss: 32421.627\nCum CV train: 0.8971   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_129', 'var_129_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5144   - loss: 325.931  - train AUC: 0.5230   - loss: 325.846 \nFold: 2 - val AUC: 0.5051   - loss: 326.056  - train AUC: 0.5217   - loss: 325.847 \nFold: 3 - val AUC: 0.5163   - loss: 325.893  - train AUC: 0.5235   - loss: 325.848 \nFold: 4 - val AUC: 0.4948   - loss: 326.312  - train AUC: 0.5238   - loss: 325.834 \nFold: 5 - val AUC: 0.5104   - loss: 326.056  - train AUC: 0.5252   - loss: 325.765 \nScore:  - val AUC: 0.5086   - loss: 32421.627 - train AUC: 0.5276   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8704   - loss: 32421.627\nCum CV train: 0.8974   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_130', 'var_130_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5243   - loss: 325.457  - train AUC: 0.5329   - loss: 325.075 \nFold: 2 - val AUC: 0.5221   - loss: 325.172  - train AUC: 0.5350   - loss: 324.989 \nFold: 3 - val AUC: 0.5399   - loss: 324.786  - train AUC: 0.5322   - loss: 325.095 \nFold: 4 - val AUC: 0.5279   - loss: 325.198  - train AUC: 0.5317   - loss: 325.064 \nFold: 5 - val AUC: 0.5227   - loss: 325.651  - train AUC: 0.5353   - loss: 324.908 \nScore:  - val AUC: 0.5267   - loss: 32421.627 - train AUC: 0.5348   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8714   - loss: 32421.627\nCum CV train: 0.8984   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_131', 'var_131_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5251   - loss: 325.831  - train AUC: 0.5367   - loss: 325.124 \nFold: 2 - val AUC: 0.5297   - loss: 325.261  - train AUC: 0.5362   - loss: 325.263 \nFold: 3 - val AUC: 0.5305   - loss: 325.348  - train AUC: 0.5355   - loss: 325.294 \nFold: 4 - val AUC: 0.5266   - loss: 325.544  - train AUC: 0.5341   - loss: 325.338 \nFold: 5 - val AUC: 0.5302   - loss: 325.442  - train AUC: 0.5342   - loss: 325.314 \nScore:  - val AUC: 0.5276   - loss: 32421.627 - train AUC: 0.5375   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8721   - loss: 32421.627\nCum CV train: 0.8991   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_132', 'var_132_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5276   - loss: 325.426  - train AUC: 0.5317   - loss: 325.344 \nFold: 2 - val AUC: 0.5204   - loss: 325.673  - train AUC: 0.5315   - loss: 325.327 \nFold: 3 - val AUC: 0.5190   - loss: 325.552  - train AUC: 0.5307   - loss: 325.404 \nFold: 4 - val AUC: 0.5222   - loss: 325.718  - train AUC: 0.5270   - loss: 325.450 \nFold: 5 - val AUC: 0.5215   - loss: 325.599  - train AUC: 0.5287   - loss: 325.385 \nScore:  - val AUC: 0.5218   - loss: 32421.627 - train AUC: 0.5316   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8727   - loss: 32421.627\nCum CV train: 0.8997   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_133', 'var_133_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5496   - loss: 323.865  - train AUC: 0.5520   - loss: 323.821 \nFold: 2 - val AUC: 0.5503   - loss: 324.168  - train AUC: 0.5504   - loss: 323.784 \nFold: 3 - val AUC: 0.5403   - loss: 324.062  - train AUC: 0.5538   - loss: 323.820 \nFold: 4 - val AUC: 0.5482   - loss: 323.916  - train AUC: 0.5518   - loss: 323.834 \nFold: 5 - val AUC: 0.5417   - loss: 324.386  - train AUC: 0.5542   - loss: 323.656 \nScore:  - val AUC: 0.5451   - loss: 32421.627 - train AUC: 0.5536   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8747   - loss: 32421.627\nCum CV train: 0.9015   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_134', 'var_134_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5221   - loss: 325.462  - train AUC: 0.5306   - loss: 325.340 \nFold: 2 - val AUC: 0.5215   - loss: 325.485  - train AUC: 0.5298   - loss: 325.358 \nFold: 3 - val AUC: 0.5136   - loss: 325.675  - train AUC: 0.5250   - loss: 325.496 \nFold: 4 - val AUC: 0.5066   - loss: 326.005  - train AUC: 0.5237   - loss: 325.418 \nFold: 5 - val AUC: 0.5185   - loss: 325.488  - train AUC: 0.5216   - loss: 325.530 \nScore:  - val AUC: 0.5165   - loss: 32421.627 - train AUC: 0.5286   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8750   - loss: 32421.627\nCum CV train: 0.9019   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_135', 'var_135_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5202   - loss: 325.230  - train AUC: 0.5330   - loss: 324.840 \nFold: 2 - val AUC: 0.5277   - loss: 325.065  - train AUC: 0.5300   - loss: 324.936 \nFold: 3 - val AUC: 0.5248   - loss: 325.094  - train AUC: 0.5329   - loss: 324.835 \nFold: 4 - val AUC: 0.5266   - loss: 325.126  - train AUC: 0.5331   - loss: 324.824 \nFold: 5 - val AUC: 0.5233   - loss: 324.984  - train AUC: 0.5320   - loss: 324.907 \nScore:  - val AUC: 0.5238   - loss: 32421.627 - train AUC: 0.5337   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8760   - loss: 32421.627\nCum CV train: 0.9028   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_136', 'var_136_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.4996   - loss: 326.216  - train AUC: 0.5188   - loss: 326.012 \nFold: 2 - val AUC: 0.5047   - loss: 326.145  - train AUC: 0.5217   - loss: 325.935 \nFold: 3 - val AUC: 0.5015   - loss: 326.199  - train AUC: 0.5233   - loss: 325.941 \nFold: 4 - val AUC: 0.5037   - loss: 326.219  - train AUC: 0.5211   - loss: 325.991 \nFold: 5 - val AUC: 0.5075   - loss: 326.166  - train AUC: 0.5210   - loss: 325.978 \nScore:  - val AUC: 0.5031   - loss: 32421.627 - train AUC: 0.5239   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8759   - loss: 32421.627\nCum CV train: 0.9030   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_137', 'var_137_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5255   - loss: 325.441  - train AUC: 0.5326   - loss: 325.244 \nFold: 2 - val AUC: 0.5350   - loss: 325.064  - train AUC: 0.5354   - loss: 325.192 \nFold: 3 - val AUC: 0.5137   - loss: 325.848  - train AUC: 0.5350   - loss: 325.095 \nFold: 4 - val AUC: 0.5339   - loss: 325.125  - train AUC: 0.5338   - loss: 325.187 \nFold: 5 - val AUC: 0.5285   - loss: 325.380  - train AUC: 0.5342   - loss: 325.163 \nScore:  - val AUC: 0.5273   - loss: 32421.627 - train AUC: 0.5354   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8767   - loss: 32421.627\nCum CV train: 0.9037   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_138', 'var_138_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5175   - loss: 325.836  - train AUC: 0.5290   - loss: 325.579 \nFold: 2 - val AUC: 0.5173   - loss: 325.712  - train AUC: 0.5286   - loss: 325.603 \nFold: 3 - val AUC: 0.5150   - loss: 325.948  - train AUC: 0.5264   - loss: 325.623 \nFold: 4 - val AUC: 0.5260   - loss: 325.735  - train AUC: 0.5294   - loss: 325.519 \nFold: 5 - val AUC: 0.5163   - loss: 325.860  - train AUC: 0.5296   - loss: 325.573 \nScore:  - val AUC: 0.5182   - loss: 32421.627 - train AUC: 0.5302   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8770   - loss: 32421.627\nCum CV train: 0.9041   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_139', 'var_139_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5785   - loss: 321.147  - train AUC: 0.5787   - loss: 321.066 \nFold: 2 - val AUC: 0.5693   - loss: 321.536  - train AUC: 0.5804   - loss: 320.958 \nFold: 3 - val AUC: 0.5772   - loss: 320.942  - train AUC: 0.5792   - loss: 321.074 \nFold: 4 - val AUC: 0.5815   - loss: 320.589  - train AUC: 0.5773   - loss: 321.215 \nFold: 5 - val AUC: 0.5729   - loss: 322.194  - train AUC: 0.5774   - loss: 320.988 \nScore:  - val AUC: 0.5751   - loss: 32421.627 - train AUC: 0.5796   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8813   - loss: 32421.627\nCum CV train: 0.9075   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_140', 'var_140_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5126   - loss: 325.986  - train AUC: 0.5215   - loss: 325.891 \nFold: 2 - val AUC: 0.5172   - loss: 325.955  - train AUC: 0.5217   - loss: 325.900 \nFold: 3 - val AUC: 0.5038   - loss: 326.253  - train AUC: 0.5231   - loss: 325.873 \nFold: 4 - val AUC: 0.5136   - loss: 326.093  - train AUC: 0.5193   - loss: 325.864 \nFold: 5 - val AUC: 0.5149   - loss: 326.031  - train AUC: 0.5227   - loss: 325.838 \nScore:  - val AUC: 0.5117   - loss: 32421.627 - train AUC: 0.5235   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8814   - loss: 32421.627\nCum CV train: 0.9077   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_141', 'var_141_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5266   - loss: 325.072  - train AUC: 0.5361   - loss: 324.824 \nFold: 2 - val AUC: 0.5342   - loss: 324.795  - train AUC: 0.5340   - loss: 324.908 \nFold: 3 - val AUC: 0.5303   - loss: 324.801  - train AUC: 0.5353   - loss: 324.839 \nFold: 4 - val AUC: 0.5259   - loss: 325.355  - train AUC: 0.5371   - loss: 324.696 \nFold: 5 - val AUC: 0.5349   - loss: 324.883  - train AUC: 0.5352   - loss: 324.810 \nScore:  - val AUC: 0.5302   - loss: 32421.627 - train AUC: 0.5369   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8825   - loss: 32421.627\nCum CV train: 0.9086   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_142', 'var_142_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5225   - loss: 325.701  - train AUC: 0.5256   - loss: 325.602 \nFold: 2 - val AUC: 0.5159   - loss: 325.886  - train AUC: 0.5294   - loss: 325.502 \nFold: 3 - val AUC: 0.5107   - loss: 326.067  - train AUC: 0.5273   - loss: 325.568 \nFold: 4 - val AUC: 0.5195   - loss: 325.678  - train AUC: 0.5254   - loss: 325.652 \nFold: 5 - val AUC: 0.5212   - loss: 325.662  - train AUC: 0.5242   - loss: 325.694 \nScore:  - val AUC: 0.5176   - loss: 32421.627 - train AUC: 0.5277   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8828   - loss: 32421.627\nCum CV train: 0.9090   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_143', 'var_143_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5033   - loss: 326.105  - train AUC: 0.5209   - loss: 325.849 \nFold: 2 - val AUC: 0.5113   - loss: 325.941  - train AUC: 0.5193   - loss: 325.874 \nFold: 3 - val AUC: 0.5016   - loss: 326.070  - train AUC: 0.5187   - loss: 325.904 \nFold: 4 - val AUC: 0.5021   - loss: 326.072  - train AUC: 0.5197   - loss: 325.868 \nFold: 5 - val AUC: 0.5067   - loss: 326.105  - train AUC: 0.5194   - loss: 325.850 \nScore:  - val AUC: 0.5049   - loss: 32421.627 - train AUC: 0.5220   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8829   - loss: 32421.627\nCum CV train: 0.9092   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_144', 'var_144_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5184   - loss: 325.794  - train AUC: 0.5295   - loss: 325.536 \nFold: 2 - val AUC: 0.5203   - loss: 325.711  - train AUC: 0.5283   - loss: 325.563 \nFold: 3 - val AUC: 0.5164   - loss: 325.874  - train AUC: 0.5285   - loss: 325.549 \nFold: 4 - val AUC: 0.5186   - loss: 325.737  - train AUC: 0.5289   - loss: 325.602 \nFold: 5 - val AUC: 0.5216   - loss: 325.856  - train AUC: 0.5272   - loss: 325.573 \nScore:  - val AUC: 0.5188   - loss: 32421.627 - train AUC: 0.5293   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8833   - loss: 32421.627\nCum CV train: 0.9097   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_145', 'var_145_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5233   - loss: 325.092  - train AUC: 0.5340   - loss: 325.004 \nFold: 2 - val AUC: 0.5314   - loss: 324.793  - train AUC: 0.5367   - loss: 324.953 \nFold: 3 - val AUC: 0.5246   - loss: 325.351  - train AUC: 0.5358   - loss: 324.903 \nFold: 4 - val AUC: 0.5262   - loss: 325.570  - train AUC: 0.5328   - loss: 324.916 \nFold: 5 - val AUC: 0.5307   - loss: 325.070  - train AUC: 0.5314   - loss: 325.032 \nScore:  - val AUC: 0.5269   - loss: 32421.627 - train AUC: 0.5353   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8840   - loss: 32421.627\nCum CV train: 0.9104   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_146', 'var_146_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5657   - loss: 322.093  - train AUC: 0.5605   - loss: 322.954 \nFold: 2 - val AUC: 0.5623   - loss: 322.796  - train AUC: 0.5620   - loss: 322.680 \nFold: 3 - val AUC: 0.5569   - loss: 322.909  - train AUC: 0.5641   - loss: 322.633 \nFold: 4 - val AUC: 0.5489   - loss: 323.670  - train AUC: 0.5633   - loss: 322.586 \nFold: 5 - val AUC: 0.5523   - loss: 323.347  - train AUC: 0.5626   - loss: 322.640 \nScore:  - val AUC: 0.5567   - loss: 32421.627 - train AUC: 0.5635   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8867   - loss: 32421.627\nCum CV train: 0.9126   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_147', 'var_147_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5416   - loss: 324.095  - train AUC: 0.5437   - loss: 323.883 \nFold: 2 - val AUC: 0.5354   - loss: 324.389  - train AUC: 0.5451   - loss: 323.803 \nFold: 3 - val AUC: 0.5399   - loss: 323.738  - train AUC: 0.5437   - loss: 324.047 \nFold: 4 - val AUC: 0.5369   - loss: 324.156  - train AUC: 0.5442   - loss: 323.905 \nFold: 5 - val AUC: 0.5427   - loss: 324.023  - train AUC: 0.5452   - loss: 323.885 \nScore:  - val AUC: 0.5388   - loss: 32421.627 - train AUC: 0.5450   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8883   - loss: 32421.627\nCum CV train: 0.9139   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_148', 'var_148_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5473   - loss: 324.066  - train AUC: 0.5591   - loss: 323.815 \nFold: 2 - val AUC: 0.5511   - loss: 324.030  - train AUC: 0.5569   - loss: 323.813 \nFold: 3 - val AUC: 0.5507   - loss: 324.125  - train AUC: 0.5587   - loss: 323.779 \nFold: 4 - val AUC: 0.5482   - loss: 324.132  - train AUC: 0.5596   - loss: 323.785 \nFold: 5 - val AUC: 0.5531   - loss: 323.902  - train AUC: 0.5569   - loss: 323.854 \nScore:  - val AUC: 0.5495   - loss: 32421.627 - train AUC: 0.5605   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8898   - loss: 32421.627\nCum CV train: 0.9151   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_149', 'var_149_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5509   - loss: 324.374  - train AUC: 0.5538   - loss: 324.374 \nFold: 2 - val AUC: 0.5529   - loss: 324.293  - train AUC: 0.5529   - loss: 324.374 \nFold: 3 - val AUC: 0.5486   - loss: 324.587  - train AUC: 0.5550   - loss: 324.269 \nFold: 4 - val AUC: 0.5494   - loss: 324.675  - train AUC: 0.5549   - loss: 324.238 \nFold: 5 - val AUC: 0.5485   - loss: 324.695  - train AUC: 0.5548   - loss: 324.233 \nScore:  - val AUC: 0.5499   - loss: 32421.627 - train AUC: 0.5551   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8913   - loss: 32421.627\nCum CV train: 0.9163   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_150', 'var_150_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5242   - loss: 325.366  - train AUC: 0.5320   - loss: 325.343 \nFold: 2 - val AUC: 0.5279   - loss: 325.590  - train AUC: 0.5352   - loss: 325.213 \nFold: 3 - val AUC: 0.5227   - loss: 325.666  - train AUC: 0.5350   - loss: 325.210 \nFold: 4 - val AUC: 0.5264   - loss: 325.289  - train AUC: 0.5324   - loss: 325.345 \nFold: 5 - val AUC: 0.5278   - loss: 325.660  - train AUC: 0.5326   - loss: 325.272 \nScore:  - val AUC: 0.5254   - loss: 32421.627 - train AUC: 0.5346   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8918   - loss: 32421.627\nCum CV train: 0.9169   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_151', 'var_151_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5203   - loss: 325.649  - train AUC: 0.5320   - loss: 325.113 \nFold: 2 - val AUC: 0.5254   - loss: 325.255  - train AUC: 0.5305   - loss: 325.201 \nFold: 3 - val AUC: 0.5242   - loss: 325.455  - train AUC: 0.5321   - loss: 325.130 \nFold: 4 - val AUC: 0.5292   - loss: 325.282  - train AUC: 0.5293   - loss: 325.220 \nFold: 5 - val AUC: 0.5257   - loss: 325.195  - train AUC: 0.5311   - loss: 325.248 \nScore:  - val AUC: 0.5246   - loss: 32421.627 - train AUC: 0.5323   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8923   - loss: 32421.627\nCum CV train: 0.9174   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_152', 'var_152_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5102   - loss: 325.896  - train AUC: 0.5206   - loss: 325.866 \nFold: 2 - val AUC: 0.5117   - loss: 325.942  - train AUC: 0.5229   - loss: 325.819 \nFold: 3 - val AUC: 0.5083   - loss: 325.938  - train AUC: 0.5199   - loss: 325.873 \nFold: 4 - val AUC: 0.5119   - loss: 326.129  - train AUC: 0.5224   - loss: 325.764 \nFold: 5 - val AUC: 0.5031   - loss: 326.082  - train AUC: 0.5203   - loss: 325.846 \nScore:  - val AUC: 0.5093   - loss: 32421.627 - train AUC: 0.5236   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8925   - loss: 32421.627\nCum CV train: 0.9176   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_153', 'var_153_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5075   - loss: 326.084  - train AUC: 0.5246   - loss: 325.934 \nFold: 2 - val AUC: 0.5058   - loss: 326.078  - train AUC: 0.5225   - loss: 325.933 \nFold: 3 - val AUC: 0.5118   - loss: 326.046  - train AUC: 0.5232   - loss: 325.914 \nFold: 4 - val AUC: 0.5014   - loss: 326.256  - train AUC: 0.5209   - loss: 325.922 \nFold: 5 - val AUC: 0.4997   - loss: 326.397  - train AUC: 0.5189   - loss: 325.934 \nScore:  - val AUC: 0.5052   - loss: 32421.627 - train AUC: 0.5274   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8925   - loss: 32421.627\nCum CV train: 0.9178   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_154', 'var_154_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5478   - loss: 323.696  - train AUC: 0.5482   - loss: 323.936 \nFold: 2 - val AUC: 0.5383   - loss: 324.168  - train AUC: 0.5478   - loss: 323.909 \nFold: 3 - val AUC: 0.5447   - loss: 324.017  - train AUC: 0.5494   - loss: 323.855 \nFold: 4 - val AUC: 0.5462   - loss: 324.189  - train AUC: 0.5466   - loss: 323.895 \nFold: 5 - val AUC: 0.5409   - loss: 324.364  - train AUC: 0.5488   - loss: 323.803 \nScore:  - val AUC: 0.5432   - loss: 32421.627 - train AUC: 0.5494   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8938   - loss: 32421.627\nCum CV train: 0.9188   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_155', 'var_155_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5232   - loss: 325.204  - train AUC: 0.5394   - loss: 324.332 \nFold: 2 - val AUC: 0.5353   - loss: 324.350  - train AUC: 0.5396   - loss: 324.404 \nFold: 3 - val AUC: 0.5352   - loss: 324.620  - train AUC: 0.5384   - loss: 324.374 \nFold: 4 - val AUC: 0.5314   - loss: 324.705  - train AUC: 0.5371   - loss: 324.463 \nFold: 5 - val AUC: 0.5369   - loss: 324.338  - train AUC: 0.5364   - loss: 324.552 \nScore:  - val AUC: 0.5320   - loss: 32421.627 - train AUC: 0.5392   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8949   - loss: 32421.627\nCum CV train: 0.9197   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_156', 'var_156_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5111   - loss: 325.971  - train AUC: 0.5263   - loss: 325.652 \nFold: 2 - val AUC: 0.5159   - loss: 325.797  - train AUC: 0.5236   - loss: 325.732 \nFold: 3 - val AUC: 0.5175   - loss: 325.849  - train AUC: 0.5248   - loss: 325.661 \nFold: 4 - val AUC: 0.5214   - loss: 325.742  - train AUC: 0.5234   - loss: 325.688 \nFold: 5 - val AUC: 0.5142   - loss: 325.930  - train AUC: 0.5265   - loss: 325.638 \nScore:  - val AUC: 0.5155   - loss: 32421.627 - train AUC: 0.5256   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8952   - loss: 32421.627\nCum CV train: 0.9200   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_157', 'var_157_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5404   - loss: 324.480  - train AUC: 0.5344   - loss: 324.998 \nFold: 2 - val AUC: 0.5255   - loss: 324.963  - train AUC: 0.5355   - loss: 324.885 \nFold: 3 - val AUC: 0.5281   - loss: 325.166  - train AUC: 0.5349   - loss: 324.857 \nFold: 4 - val AUC: 0.5264   - loss: 325.300  - train AUC: 0.5353   - loss: 324.819 \nFold: 5 - val AUC: 0.5229   - loss: 325.360  - train AUC: 0.5368   - loss: 324.791 \nScore:  - val AUC: 0.5283   - loss: 32421.627 - train AUC: 0.5364   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8960   - loss: 32421.627\nCum CV train: 0.9207   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_158', 'var_158_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.4943   - loss: 326.202  - train AUC: 0.5188   - loss: 326.037 \nFold: 2 - val AUC: 0.5079   - loss: 326.133  - train AUC: 0.5203   - loss: 325.982 \nFold: 3 - val AUC: 0.4967   - loss: 326.262  - train AUC: 0.5188   - loss: 326.035 \nFold: 4 - val AUC: 0.5009   - loss: 326.198  - train AUC: 0.5208   - loss: 326.033 \nFold: 5 - val AUC: 0.4990   - loss: 326.199  - train AUC: 0.5199   - loss: 325.983 \nScore:  - val AUC: 0.4999   - loss: 32421.627 - train AUC: 0.5235   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8960   - loss: 32421.627\nCum CV train: 0.9208   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_159', 'var_159_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5215   - loss: 325.830  - train AUC: 0.5227   - loss: 325.893 \nFold: 2 - val AUC: 0.4998   - loss: 326.362  - train AUC: 0.5240   - loss: 325.836 \nFold: 3 - val AUC: 0.5155   - loss: 326.060  - train AUC: 0.5208   - loss: 325.888 \nFold: 4 - val AUC: 0.5116   - loss: 326.115  - train AUC: 0.5229   - loss: 325.846 \nFold: 5 - val AUC: 0.5150   - loss: 326.036  - train AUC: 0.5211   - loss: 325.890 \nScore:  - val AUC: 0.5115   - loss: 32421.627 - train AUC: 0.5239   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8960   - loss: 32421.627\nCum CV train: 0.9210   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_160', 'var_160_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5011   - loss: 326.221  - train AUC: 0.5217   - loss: 325.916 \nFold: 2 - val AUC: 0.5013   - loss: 326.101  - train AUC: 0.5195   - loss: 325.911 \nFold: 3 - val AUC: 0.5115   - loss: 326.013  - train AUC: 0.5282   - loss: 325.722 \nFold: 4 - val AUC: 0.5135   - loss: 325.970  - train AUC: 0.5262   - loss: 325.800 \nFold: 5 - val AUC: 0.5074   - loss: 326.087  - train AUC: 0.5226   - loss: 325.870 \nScore:  - val AUC: 0.5070   - loss: 32421.627 - train AUC: 0.5291   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8961   - loss: 32421.627\nCum CV train: 0.9212   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_161', 'var_161_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5034   - loss: 326.196  - train AUC: 0.5204   - loss: 325.984 \nFold: 2 - val AUC: 0.5005   - loss: 326.150  - train AUC: 0.5231   - loss: 325.944 \nFold: 3 - val AUC: 0.5061   - loss: 326.196  - train AUC: 0.5231   - loss: 325.930 \nFold: 4 - val AUC: 0.5059   - loss: 326.189  - train AUC: 0.5219   - loss: 325.919 \nFold: 5 - val AUC: 0.5038   - loss: 326.168  - train AUC: 0.5237   - loss: 325.911 \nScore:  - val AUC: 0.5034   - loss: 32421.627 - train AUC: 0.5258   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8961   - loss: 32421.627\nCum CV train: 0.9214   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_162', 'var_162_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5254   - loss: 325.237  - train AUC: 0.5367   - loss: 325.070 \nFold: 2 - val AUC: 0.5353   - loss: 325.076  - train AUC: 0.5352   - loss: 325.046 \nFold: 3 - val AUC: 0.5317   - loss: 325.214  - train AUC: 0.5351   - loss: 325.097 \nFold: 4 - val AUC: 0.5330   - loss: 325.157  - train AUC: 0.5364   - loss: 325.025 \nFold: 5 - val AUC: 0.5213   - loss: 325.650  - train AUC: 0.5373   - loss: 324.949 \nScore:  - val AUC: 0.5289   - loss: 32421.627 - train AUC: 0.5373   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8967   - loss: 32421.627\nCum CV train: 0.9219   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_163', 'var_163_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5319   - loss: 324.844  - train AUC: 0.5393   - loss: 324.665 \nFold: 2 - val AUC: 0.5309   - loss: 324.850  - train AUC: 0.5394   - loss: 324.697 \nFold: 3 - val AUC: 0.5321   - loss: 324.916  - train AUC: 0.5394   - loss: 324.658 \nFold: 4 - val AUC: 0.5316   - loss: 325.017  - train AUC: 0.5367   - loss: 324.776 \nFold: 5 - val AUC: 0.5334   - loss: 324.878  - train AUC: 0.5385   - loss: 324.699 \nScore:  - val AUC: 0.5315   - loss: 32421.627 - train AUC: 0.5397   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8976   - loss: 32421.627\nCum CV train: 0.9227   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_164', 'var_164_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5424   - loss: 323.680  - train AUC: 0.5445   - loss: 323.737 \nFold: 2 - val AUC: 0.5358   - loss: 323.631  - train AUC: 0.5443   - loss: 323.763 \nFold: 3 - val AUC: 0.5345   - loss: 324.787  - train AUC: 0.5444   - loss: 323.545 \nFold: 4 - val AUC: 0.5338   - loss: 323.814  - train AUC: 0.5477   - loss: 323.678 \nFold: 5 - val AUC: 0.5450   - loss: 323.459  - train AUC: 0.5429   - loss: 323.802 \nScore:  - val AUC: 0.5376   - loss: 32421.627 - train AUC: 0.5463   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.8991   - loss: 32421.627\nCum CV train: 0.9238   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_165', 'var_165_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5506   - loss: 323.223  - train AUC: 0.5577   - loss: 323.340 \nFold: 2 - val AUC: 0.5475   - loss: 323.779  - train AUC: 0.5574   - loss: 323.233 \nFold: 3 - val AUC: 0.5517   - loss: 323.825  - train AUC: 0.5556   - loss: 323.276 \nFold: 4 - val AUC: 0.5497   - loss: 323.728  - train AUC: 0.5571   - loss: 323.243 \nFold: 5 - val AUC: 0.5653   - loss: 323.044  - train AUC: 0.5540   - loss: 323.377 \nScore:  - val AUC: 0.5522   - loss: 32421.627 - train AUC: 0.5568   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9011   - loss: 32421.627\nCum CV train: 0.9254   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_166', 'var_166_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5461   - loss: 324.039  - train AUC: 0.5576   - loss: 323.282 \nFold: 2 - val AUC: 0.5401   - loss: 324.493  - train AUC: 0.5569   - loss: 323.320 \nFold: 3 - val AUC: 0.5515   - loss: 323.261  - train AUC: 0.5577   - loss: 323.420 \nFold: 4 - val AUC: 0.5532   - loss: 323.600  - train AUC: 0.5568   - loss: 323.382 \nFold: 5 - val AUC: 0.5678   - loss: 322.656  - train AUC: 0.5569   - loss: 323.452 \nScore:  - val AUC: 0.5514   - loss: 32421.627 - train AUC: 0.5592   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9028   - loss: 32421.627\nCum CV train: 0.9268   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_167', 'var_167_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5371   - loss: 325.030  - train AUC: 0.5362   - loss: 324.989 \nFold: 2 - val AUC: 0.5326   - loss: 324.968  - train AUC: 0.5392   - loss: 324.880 \nFold: 3 - val AUC: 0.5298   - loss: 325.272  - train AUC: 0.5364   - loss: 324.931 \nFold: 4 - val AUC: 0.5325   - loss: 324.977  - train AUC: 0.5341   - loss: 325.093 \nFold: 5 - val AUC: 0.5279   - loss: 325.464  - train AUC: 0.5364   - loss: 324.943 \nScore:  - val AUC: 0.5316   - loss: 32421.627 - train AUC: 0.5374   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9035   - loss: 32421.627\nCum CV train: 0.9274   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_168', 'var_168_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5113   - loss: 325.919  - train AUC: 0.5260   - loss: 325.697 \nFold: 2 - val AUC: 0.5071   - loss: 326.010  - train AUC: 0.5249   - loss: 325.692 \nFold: 3 - val AUC: 0.5170   - loss: 325.803  - train AUC: 0.5252   - loss: 325.716 \nFold: 4 - val AUC: 0.5231   - loss: 325.721  - train AUC: 0.5297   - loss: 325.616 \nFold: 5 - val AUC: 0.5133   - loss: 325.926  - train AUC: 0.5235   - loss: 325.739 \nScore:  - val AUC: 0.5148   - loss: 32421.627 - train AUC: 0.5289   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9038   - loss: 32421.627\nCum CV train: 0.9277   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_169', 'var_169_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5481   - loss: 324.627  - train AUC: 0.5524   - loss: 324.418 \nFold: 2 - val AUC: 0.5451   - loss: 324.777  - train AUC: 0.5529   - loss: 324.417 \nFold: 3 - val AUC: 0.5498   - loss: 324.387  - train AUC: 0.5509   - loss: 324.492 \nFold: 4 - val AUC: 0.5449   - loss: 324.750  - train AUC: 0.5520   - loss: 324.404 \nFold: 5 - val AUC: 0.5482   - loss: 324.563  - train AUC: 0.5528   - loss: 324.377 \nScore:  - val AUC: 0.5465   - loss: 32421.627 - train AUC: 0.5537   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9049   - loss: 32421.627\nCum CV train: 0.9286   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_170', 'var_170_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5412   - loss: 324.220  - train AUC: 0.5452   - loss: 323.642 \nFold: 2 - val AUC: 0.5440   - loss: 323.620  - train AUC: 0.5457   - loss: 323.716 \nFold: 3 - val AUC: 0.5374   - loss: 323.752  - train AUC: 0.5467   - loss: 323.693 \nFold: 4 - val AUC: 0.5415   - loss: 323.924  - train AUC: 0.5463   - loss: 323.621 \nFold: 5 - val AUC: 0.5408   - loss: 323.867  - train AUC: 0.5457   - loss: 323.662 \nScore:  - val AUC: 0.5406   - loss: 32421.627 - train AUC: 0.5466   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9063   - loss: 32421.627\nCum CV train: 0.9297   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_171', 'var_171_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5171   - loss: 325.623  - train AUC: 0.5278   - loss: 325.671 \nFold: 2 - val AUC: 0.5103   - loss: 326.003  - train AUC: 0.5244   - loss: 325.645 \nFold: 3 - val AUC: 0.5111   - loss: 325.878  - train AUC: 0.5223   - loss: 325.713 \nFold: 4 - val AUC: 0.5116   - loss: 325.900  - train AUC: 0.5265   - loss: 325.636 \nFold: 5 - val AUC: 0.5148   - loss: 325.923  - train AUC: 0.5235   - loss: 325.718 \nScore:  - val AUC: 0.5125   - loss: 32421.627 - train AUC: 0.5272   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9065   - loss: 32421.627\nCum CV train: 0.9300   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_172', 'var_172_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5414   - loss: 324.392  - train AUC: 0.5423   - loss: 324.657 \nFold: 2 - val AUC: 0.5339   - loss: 325.084  - train AUC: 0.5449   - loss: 324.478 \nFold: 3 - val AUC: 0.5325   - loss: 325.067  - train AUC: 0.5434   - loss: 324.513 \nFold: 4 - val AUC: 0.5404   - loss: 324.614  - train AUC: 0.5433   - loss: 324.513 \nFold: 5 - val AUC: 0.5426   - loss: 324.598  - train AUC: 0.5422   - loss: 324.587 \nScore:  - val AUC: 0.5377   - loss: 32421.627 - train AUC: 0.5440   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9075   - loss: 32421.627\nCum CV train: 0.9308   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_173', 'var_173_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5387   - loss: 324.779  - train AUC: 0.5443   - loss: 324.335 \nFold: 2 - val AUC: 0.5350   - loss: 324.622  - train AUC: 0.5457   - loss: 324.372 \nFold: 3 - val AUC: 0.5376   - loss: 324.594  - train AUC: 0.5442   - loss: 324.472 \nFold: 4 - val AUC: 0.5448   - loss: 324.241  - train AUC: 0.5444   - loss: 324.427 \nFold: 5 - val AUC: 0.5396   - loss: 324.770  - train AUC: 0.5434   - loss: 324.405 \nScore:  - val AUC: 0.5388   - loss: 32421.627 - train AUC: 0.5453   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9084   - loss: 32421.627\nCum CV train: 0.9316   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_174', 'var_174_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5479   - loss: 323.052  - train AUC: 0.5598   - loss: 322.160 \nFold: 2 - val AUC: 0.5570   - loss: 322.729  - train AUC: 0.5566   - loss: 322.308 \nFold: 3 - val AUC: 0.5532   - loss: 322.694  - train AUC: 0.5582   - loss: 322.274 \nFold: 4 - val AUC: 0.5533   - loss: 321.753  - train AUC: 0.5593   - loss: 322.454 \nFold: 5 - val AUC: 0.5573   - loss: 322.530  - train AUC: 0.5577   - loss: 322.307 \nScore:  - val AUC: 0.5535   - loss: 32421.627 - train AUC: 0.5591   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9103   - loss: 32421.627\nCum CV train: 0.9330   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_175', 'var_175_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5173   - loss: 325.703  - train AUC: 0.5304   - loss: 325.492 \nFold: 2 - val AUC: 0.5221   - loss: 325.731  - train AUC: 0.5284   - loss: 325.543 \nFold: 3 - val AUC: 0.5148   - loss: 325.968  - train AUC: 0.5302   - loss: 325.476 \nFold: 4 - val AUC: 0.5267   - loss: 325.601  - train AUC: 0.5275   - loss: 325.575 \nFold: 5 - val AUC: 0.5221   - loss: 325.709  - train AUC: 0.5310   - loss: 325.498 \nScore:  - val AUC: 0.5198   - loss: 32421.627 - train AUC: 0.5314   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9107   - loss: 32421.627\nCum CV train: 0.9334   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_176', 'var_176_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.4974   - loss: 326.206  - train AUC: 0.5212   - loss: 325.959 \nFold: 2 - val AUC: 0.5094   - loss: 326.084  - train AUC: 0.5238   - loss: 325.933 \nFold: 3 - val AUC: 0.5069   - loss: 326.186  - train AUC: 0.5220   - loss: 325.911 \nFold: 4 - val AUC: 0.5037   - loss: 326.196  - train AUC: 0.5201   - loss: 325.949 \nFold: 5 - val AUC: 0.4996   - loss: 326.258  - train AUC: 0.5232   - loss: 325.939 \nScore:  - val AUC: 0.5029   - loss: 32421.627 - train AUC: 0.5246   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9107   - loss: 32421.627\nCum CV train: 0.9335   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_177', 'var_177_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5315   - loss: 324.497  - train AUC: 0.5430   - loss: 324.182 \nFold: 2 - val AUC: 0.5368   - loss: 324.473  - train AUC: 0.5433   - loss: 324.153 \nFold: 3 - val AUC: 0.5314   - loss: 324.787  - train AUC: 0.5427   - loss: 324.119 \nFold: 4 - val AUC: 0.5338   - loss: 324.490  - train AUC: 0.5437   - loss: 324.210 \nFold: 5 - val AUC: 0.5397   - loss: 323.946  - train AUC: 0.5422   - loss: 324.310 \nScore:  - val AUC: 0.5345   - loss: 32421.627 - train AUC: 0.5440   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9118   - loss: 32421.627\nCum CV train: 0.9344   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_178', 'var_178_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5245   - loss: 325.609  - train AUC: 0.5292   - loss: 325.563 \nFold: 2 - val AUC: 0.5185   - loss: 325.809  - train AUC: 0.5286   - loss: 325.586 \nFold: 3 - val AUC: 0.5183   - loss: 325.762  - train AUC: 0.5289   - loss: 325.566 \nFold: 4 - val AUC: 0.5189   - loss: 325.817  - train AUC: 0.5275   - loss: 325.583 \nFold: 5 - val AUC: 0.5167   - loss: 326.044  - train AUC: 0.5266   - loss: 325.606 \nScore:  - val AUC: 0.5184   - loss: 32421.627 - train AUC: 0.5292   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9120   - loss: 32421.627\nCum CV train: 0.9347   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_179', 'var_179_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5334   - loss: 324.172  - train AUC: 0.5516   - loss: 323.235 \nFold: 2 - val AUC: 0.5440   - loss: 323.465  - train AUC: 0.5503   - loss: 323.311 \nFold: 3 - val AUC: 0.5487   - loss: 323.117  - train AUC: 0.5487   - loss: 323.439 \nFold: 4 - val AUC: 0.5489   - loss: 323.039  - train AUC: 0.5512   - loss: 323.332 \nFold: 5 - val AUC: 0.5438   - loss: 323.777  - train AUC: 0.5494   - loss: 323.296 \nScore:  - val AUC: 0.5435   - loss: 32421.627 - train AUC: 0.5514   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9133   - loss: 32421.627\nCum CV train: 0.9356   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_180', 'var_180_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5291   - loss: 324.865  - train AUC: 0.5375   - loss: 324.880 \nFold: 2 - val AUC: 0.5304   - loss: 325.142  - train AUC: 0.5356   - loss: 324.835 \nFold: 3 - val AUC: 0.5288   - loss: 324.724  - train AUC: 0.5337   - loss: 324.998 \nFold: 4 - val AUC: 0.5218   - loss: 325.487  - train AUC: 0.5357   - loss: 324.808 \nFold: 5 - val AUC: 0.5269   - loss: 325.318  - train AUC: 0.5373   - loss: 324.782 \nScore:  - val AUC: 0.5274   - loss: 32421.627 - train AUC: 0.5375   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9139   - loss: 32421.627\nCum CV train: 0.9362   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_181', 'var_181_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5126   - loss: 326.019  - train AUC: 0.5262   - loss: 325.747 \nFold: 2 - val AUC: 0.5103   - loss: 325.985  - train AUC: 0.5272   - loss: 325.785 \nFold: 3 - val AUC: 0.5135   - loss: 326.060  - train AUC: 0.5265   - loss: 325.729 \nFold: 4 - val AUC: 0.5130   - loss: 326.006  - train AUC: 0.5260   - loss: 325.718 \nFold: 5 - val AUC: 0.5103   - loss: 325.968  - train AUC: 0.5250   - loss: 325.802 \nScore:  - val AUC: 0.5120   - loss: 32421.627 - train AUC: 0.5285   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9140   - loss: 32421.627\nCum CV train: 0.9363   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_182', 'var_182_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5093   - loss: 326.078  - train AUC: 0.5205   - loss: 325.899 \nFold: 2 - val AUC: 0.5026   - loss: 326.125  - train AUC: 0.5203   - loss: 325.940 \nFold: 3 - val AUC: 0.4992   - loss: 326.123  - train AUC: 0.5190   - loss: 325.946 \nFold: 4 - val AUC: 0.5082   - loss: 326.098  - train AUC: 0.5200   - loss: 325.911 \nFold: 5 - val AUC: 0.5001   - loss: 326.160  - train AUC: 0.5212   - loss: 325.872 \nScore:  - val AUC: 0.5030   - loss: 32421.627 - train AUC: 0.5234   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9141   - loss: 32421.627\nCum CV train: 0.9365   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_183', 'var_183_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5012   - loss: 326.117  - train AUC: 0.5168   - loss: 326.025 \nFold: 2 - val AUC: 0.4989   - loss: 326.128  - train AUC: 0.5189   - loss: 325.995 \nFold: 3 - val AUC: 0.4979   - loss: 326.204  - train AUC: 0.5192   - loss: 325.966 \nFold: 4 - val AUC: 0.4984   - loss: 326.213  - train AUC: 0.5190   - loss: 325.958 \nFold: 5 - val AUC: 0.4938   - loss: 326.263  - train AUC: 0.5208   - loss: 325.997 \nScore:  - val AUC: 0.4980   - loss: 32421.627 - train AUC: 0.5237   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9141   - loss: 32421.627\nCum CV train: 0.9366   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_184', 'var_184_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5488   - loss: 324.150  - train AUC: 0.5531   - loss: 323.529 \nFold: 2 - val AUC: 0.5472   - loss: 323.822  - train AUC: 0.5535   - loss: 323.697 \nFold: 3 - val AUC: 0.5486   - loss: 323.560  - train AUC: 0.5527   - loss: 323.747 \nFold: 4 - val AUC: 0.5425   - loss: 324.249  - train AUC: 0.5560   - loss: 323.541 \nFold: 5 - val AUC: 0.5533   - loss: 323.650  - train AUC: 0.5529   - loss: 323.650 \nScore:  - val AUC: 0.5476   - loss: 32421.627 - train AUC: 0.5551   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9155   - loss: 32421.627\nCum CV train: 0.9378   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_185', 'var_185_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5007   - loss: 326.139  - train AUC: 0.5194   - loss: 325.983 \nFold: 2 - val AUC: 0.4999   - loss: 326.151  - train AUC: 0.5208   - loss: 326.001 \nFold: 3 - val AUC: 0.4974   - loss: 326.233  - train AUC: 0.5171   - loss: 326.039 \nFold: 4 - val AUC: 0.4955   - loss: 326.223  - train AUC: 0.5176   - loss: 326.010 \nFold: 5 - val AUC: 0.4972   - loss: 326.218  - train AUC: 0.5180   - loss: 326.042 \nScore:  - val AUC: 0.4982   - loss: 32421.627 - train AUC: 0.5238   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9155   - loss: 32421.627\nCum CV train: 0.9378   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_186', 'var_186_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5301   - loss: 325.447  - train AUC: 0.5354   - loss: 325.285 \nFold: 2 - val AUC: 0.5287   - loss: 325.454  - train AUC: 0.5362   - loss: 325.263 \nFold: 3 - val AUC: 0.5346   - loss: 325.649  - train AUC: 0.5333   - loss: 325.300 \nFold: 4 - val AUC: 0.5279   - loss: 325.446  - train AUC: 0.5361   - loss: 325.263 \nFold: 5 - val AUC: 0.5253   - loss: 325.551  - train AUC: 0.5368   - loss: 325.258 \nScore:  - val AUC: 0.5282   - loss: 32421.627 - train AUC: 0.5366   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9158   - loss: 32421.627\nCum CV train: 0.9382   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_187', 'var_187_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5099   - loss: 325.819  - train AUC: 0.5281   - loss: 325.373 \nFold: 2 - val AUC: 0.5131   - loss: 325.804  - train AUC: 0.5231   - loss: 325.453 \nFold: 3 - val AUC: 0.5192   - loss: 325.402  - train AUC: 0.5240   - loss: 325.535 \nFold: 4 - val AUC: 0.5090   - loss: 325.681  - train AUC: 0.5260   - loss: 325.431 \nFold: 5 - val AUC: 0.5119   - loss: 325.689  - train AUC: 0.5240   - loss: 325.467 \nScore:  - val AUC: 0.5124   - loss: 32421.627 - train AUC: 0.5276   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9162   - loss: 32421.627\nCum CV train: 0.9386   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_188', 'var_188_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5361   - loss: 324.727  - train AUC: 0.5376   - loss: 324.650 \nFold: 2 - val AUC: 0.5328   - loss: 325.409  - train AUC: 0.5355   - loss: 324.564 \nFold: 3 - val AUC: 0.5353   - loss: 324.378  - train AUC: 0.5360   - loss: 324.767 \nFold: 4 - val AUC: 0.5255   - loss: 324.962  - train AUC: 0.5382   - loss: 324.618 \nFold: 5 - val AUC: 0.5271   - loss: 324.841  - train AUC: 0.5370   - loss: 324.671 \nScore:  - val AUC: 0.5309   - loss: 32421.627 - train AUC: 0.5375   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9170   - loss: 32421.627\nCum CV train: 0.9393   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_189', 'var_189_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5080   - loss: 326.150  - train AUC: 0.5196   - loss: 325.928 \nFold: 2 - val AUC: 0.5063   - loss: 326.116  - train AUC: 0.5234   - loss: 325.913 \nFold: 3 - val AUC: 0.5106   - loss: 326.137  - train AUC: 0.5232   - loss: 325.850 \nFold: 4 - val AUC: 0.5061   - loss: 326.145  - train AUC: 0.5221   - loss: 325.896 \nFold: 5 - val AUC: 0.5117   - loss: 326.062  - train AUC: 0.5256   - loss: 325.827 \nScore:  - val AUC: 0.5084   - loss: 32421.627 - train AUC: 0.5251   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9171   - loss: 32421.627\nCum CV train: 0.9394   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_190', 'var_190_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5565   - loss: 323.726  - train AUC: 0.5550   - loss: 323.311 \nFold: 2 - val AUC: 0.5549   - loss: 323.461  - train AUC: 0.5559   - loss: 323.300 \nFold: 3 - val AUC: 0.5450   - loss: 323.677  - train AUC: 0.5584   - loss: 323.239 \nFold: 4 - val AUC: 0.5560   - loss: 323.386  - train AUC: 0.5544   - loss: 323.439 \nFold: 5 - val AUC: 0.5495   - loss: 323.488  - train AUC: 0.5578   - loss: 323.310 \nScore:  - val AUC: 0.5515   - loss: 32421.627 - train AUC: 0.5572   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9186   - loss: 32421.627\nCum CV train: 0.9405   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_191', 'var_191_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5474   - loss: 324.137  - train AUC: 0.5468   - loss: 324.116 \nFold: 2 - val AUC: 0.5433   - loss: 323.905  - train AUC: 0.5506   - loss: 323.999 \nFold: 3 - val AUC: 0.5454   - loss: 324.398  - train AUC: 0.5505   - loss: 323.863 \nFold: 4 - val AUC: 0.5369   - loss: 324.668  - train AUC: 0.5503   - loss: 323.918 \nFold: 5 - val AUC: 0.5486   - loss: 323.949  - train AUC: 0.5488   - loss: 324.013 \nScore:  - val AUC: 0.5438   - loss: 32421.627 - train AUC: 0.5502   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9196   - loss: 32421.627\nCum CV train: 0.9413   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_192', 'var_192_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5388   - loss: 325.245  - train AUC: 0.5489   - loss: 324.858 \nFold: 2 - val AUC: 0.5461   - loss: 324.937  - train AUC: 0.5483   - loss: 324.864 \nFold: 3 - val AUC: 0.5407   - loss: 325.186  - train AUC: 0.5487   - loss: 324.854 \nFold: 4 - val AUC: 0.5521   - loss: 324.650  - train AUC: 0.5472   - loss: 324.924 \nFold: 5 - val AUC: 0.5394   - loss: 325.248  - train AUC: 0.5490   - loss: 324.818 \nScore:  - val AUC: 0.5431   - loss: 32421.627 - train AUC: 0.5497   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9203   - loss: 32421.627\nCum CV train: 0.9419   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_193', 'var_193_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5128   - loss: 325.924  - train AUC: 0.5254   - loss: 325.673 \nFold: 2 - val AUC: 0.5208   - loss: 325.612  - train AUC: 0.5276   - loss: 325.655 \nFold: 3 - val AUC: 0.5136   - loss: 326.070  - train AUC: 0.5242   - loss: 325.702 \nFold: 4 - val AUC: 0.5121   - loss: 326.047  - train AUC: 0.5250   - loss: 325.687 \nFold: 5 - val AUC: 0.5161   - loss: 325.898  - train AUC: 0.5237   - loss: 325.750 \nScore:  - val AUC: 0.5150   - loss: 32421.627 - train AUC: 0.5273   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9205   - loss: 32421.627\nCum CV train: 0.9421   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_194', 'var_194_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5142   - loss: 325.912  - train AUC: 0.5282   - loss: 325.480 \nFold: 2 - val AUC: 0.5183   - loss: 325.589  - train AUC: 0.5295   - loss: 325.514 \nFold: 3 - val AUC: 0.5166   - loss: 325.954  - train AUC: 0.5264   - loss: 325.522 \nFold: 4 - val AUC: 0.5194   - loss: 325.711  - train AUC: 0.5266   - loss: 325.533 \nFold: 5 - val AUC: 0.5271   - loss: 325.542  - train AUC: 0.5256   - loss: 325.566 \nScore:  - val AUC: 0.5185   - loss: 32421.627 - train AUC: 0.5288   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9206   - loss: 32421.627\nCum CV train: 0.9423   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_195', 'var_195_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5204   - loss: 325.698  - train AUC: 0.5346   - loss: 325.268 \nFold: 2 - val AUC: 0.5305   - loss: 325.501  - train AUC: 0.5329   - loss: 325.336 \nFold: 3 - val AUC: 0.5287   - loss: 325.297  - train AUC: 0.5337   - loss: 325.381 \nFold: 4 - val AUC: 0.5265   - loss: 325.618  - train AUC: 0.5363   - loss: 325.156 \nFold: 5 - val AUC: 0.5216   - loss: 325.556  - train AUC: 0.5347   - loss: 325.316 \nScore:  - val AUC: 0.5254   - loss: 32421.627 - train AUC: 0.5357   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9210   - loss: 32421.627\nCum CV train: 0.9427   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_196', 'var_196_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5185   - loss: 325.599  - train AUC: 0.5304   - loss: 325.266 \nFold: 2 - val AUC: 0.5247   - loss: 325.409  - train AUC: 0.5299   - loss: 325.232 \nFold: 3 - val AUC: 0.5167   - loss: 325.746  - train AUC: 0.5280   - loss: 325.340 \nFold: 4 - val AUC: 0.5208   - loss: 325.405  - train AUC: 0.5310   - loss: 325.283 \nFold: 5 - val AUC: 0.5225   - loss: 325.347  - train AUC: 0.5280   - loss: 325.335 \nScore:  - val AUC: 0.5209   - loss: 32421.627 - train AUC: 0.5307   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9213   - loss: 32421.627\nCum CV train: 0.9430   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_197', 'var_197_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5268   - loss: 325.277  - train AUC: 0.5387   - loss: 324.929 \nFold: 2 - val AUC: 0.5267   - loss: 325.401  - train AUC: 0.5368   - loss: 324.943 \nFold: 3 - val AUC: 0.5335   - loss: 325.043  - train AUC: 0.5367   - loss: 324.943 \nFold: 4 - val AUC: 0.5329   - loss: 325.170  - train AUC: 0.5364   - loss: 324.934 \nFold: 5 - val AUC: 0.5330   - loss: 325.076  - train AUC: 0.5349   - loss: 325.042 \nScore:  - val AUC: 0.5304   - loss: 32421.627 - train AUC: 0.5377   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9218   - loss: 32421.627\nCum CV train: 0.9435   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_198', 'var_198_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5507   - loss: 323.428  - train AUC: 0.5556   - loss: 323.296 \nFold: 2 - val AUC: 0.5492   - loss: 323.431  - train AUC: 0.5550   - loss: 323.283 \nFold: 3 - val AUC: 0.5494   - loss: 323.893  - train AUC: 0.5543   - loss: 323.238 \nFold: 4 - val AUC: 0.5489   - loss: 323.538  - train AUC: 0.5563   - loss: 323.232 \nFold: 5 - val AUC: 0.5541   - loss: 323.162  - train AUC: 0.5550   - loss: 323.330 \nScore:  - val AUC: 0.5502   - loss: 32421.627 - train AUC: 0.5559   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9232   - loss: 32421.627\nCum CV train: 0.9445   - loss: 32421.627\n**************************************************\n\nTraining on: ['var_199', 'var_199_count']\n\nsetting:  4\nFold: 1 - val AUC: 0.5251   - loss: 325.606  - train AUC: 0.5314   - loss: 325.392 \nFold: 2 - val AUC: 0.5246   - loss: 325.725  - train AUC: 0.5354   - loss: 325.331 \nFold: 3 - val AUC: 0.5318   - loss: 325.480  - train AUC: 0.5374   - loss: 325.293 \nFold: 4 - val AUC: 0.5254   - loss: 325.461  - train AUC: 0.5363   - loss: 325.352 \nFold: 5 - val AUC: 0.5205   - loss: 325.810  - train AUC: 0.5352   - loss: 325.354 \nScore:  - val AUC: 0.5248   - loss: 32421.627 - train AUC: 0.5376   - loss: 32421.627\n\nbest setting:  4\nCum CV val  : 0.9236   - loss: 32421.627\nCum CV train: 0.9449   - loss: 32421.627\n**************************************************\n\n","output_type":"stream"}],"execution_count":23},{"cell_type":"markdown","source":"# 得到集成在train和val上的效果","metadata":{}},{"cell_type":"code","source":"preds_oof_cum = np.zeros(preds_oof.shape[0])\npreds_train_cum = np.zeros(preds_train.shape[0])\nfor i in range(len(features)):\n    preds_oof_cum += preds_oof[:,i]\n    preds_train_cum += preds_train[:,i]\n    print(\"var_{} Cum val: {:<8.5f}\".format(i,roc_auc_score(target_train, preds_oof_cum)), end=\"\")\n    print(\" - train: {:<8.5f}\".format(roc_auc_score(target_train, preds_train_cum)))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T09:35:53.947150Z","iopub.execute_input":"2025-09-16T09:35:53.948093Z","iopub.status.idle":"2025-09-16T09:36:22.616460Z","shell.execute_reply.started":"2025-09-16T09:35:53.948058Z","shell.execute_reply":"2025-09-16T09:36:22.615399Z"}},"outputs":[{"name":"stdout","text":"var_0 Cum val: 0.54783  - train: 0.55630 \nvar_1 Cum val: 0.57412  - train: 0.58131 \nvar_2 Cum val: 0.59680  - train: 0.60423 \nvar_3 Cum val: 0.59810  - train: 0.60724 \nvar_4 Cum val: 0.59855  - train: 0.60964 \nvar_5 Cum val: 0.60787  - train: 0.61929 \nvar_6 Cum val: 0.62819  - train: 0.63856 \nvar_7 Cum val: 0.62790  - train: 0.63969 \nvar_8 Cum val: 0.62993  - train: 0.64265 \nvar_9 Cum val: 0.64039  - train: 0.65360 \nvar_10 Cum val: 0.64064  - train: 0.65471 \nvar_11 Cum val: 0.64308  - train: 0.65831 \nvar_12 Cum val: 0.65991  - train: 0.67425 \nvar_13 Cum val: 0.67113  - train: 0.68519 \nvar_14 Cum val: 0.67123  - train: 0.68634 \nvar_15 Cum val: 0.67218  - train: 0.68808 \nvar_16 Cum val: 0.67227  - train: 0.68920 \nvar_17 Cum val: 0.67205  - train: 0.68989 \nvar_18 Cum val: 0.67979  - train: 0.69767 \nvar_19 Cum val: 0.68089  - train: 0.69931 \nvar_20 Cum val: 0.68281  - train: 0.70183 \nvar_21 Cum val: 0.69348  - train: 0.71204 \nvar_22 Cum val: 0.70299  - train: 0.72109 \nvar_23 Cum val: 0.70435  - train: 0.72303 \nvar_24 Cum val: 0.70647  - train: 0.72567 \nvar_25 Cum val: 0.70667  - train: 0.72653 \nvar_26 Cum val: 0.71752  - train: 0.73663 \nvar_27 Cum val: 0.71727  - train: 0.73712 \nvar_28 Cum val: 0.71855  - train: 0.73892 \nvar_29 Cum val: 0.71841  - train: 0.73939 \nvar_30 Cum val: 0.71824  - train: 0.73980 \nvar_31 Cum val: 0.71938  - train: 0.74145 \nvar_32 Cum val: 0.72236  - train: 0.74461 \nvar_33 Cum val: 0.72826  - train: 0.75023 \nvar_34 Cum val: 0.73330  - train: 0.75513 \nvar_35 Cum val: 0.73652  - train: 0.75853 \nvar_36 Cum val: 0.73951  - train: 0.76181 \nvar_37 Cum val: 0.73973  - train: 0.76256 \nvar_38 Cum val: 0.73966  - train: 0.76297 \nvar_39 Cum val: 0.73963  - train: 0.76346 \nvar_40 Cum val: 0.74569  - train: 0.76911 \nvar_41 Cum val: 0.74564  - train: 0.76948 \nvar_42 Cum val: 0.74561  - train: 0.77007 \nvar_43 Cum val: 0.74711  - train: 0.77183 \nvar_44 Cum val: 0.75403  - train: 0.77800 \nvar_45 Cum val: 0.75530  - train: 0.77961 \nvar_46 Cum val: 0.75536  - train: 0.78007 \nvar_47 Cum val: 0.75529  - train: 0.78054 \nvar_48 Cum val: 0.75745  - train: 0.78282 \nvar_49 Cum val: 0.76010  - train: 0.78554 \nvar_50 Cum val: 0.76044  - train: 0.78626 \nvar_51 Cum val: 0.76262  - train: 0.78847 \nvar_52 Cum val: 0.76464  - train: 0.79063 \nvar_53 Cum val: 0.77146  - train: 0.79693 \nvar_54 Cum val: 0.77206  - train: 0.79778 \nvar_55 Cum val: 0.77276  - train: 0.79883 \nvar_56 Cum val: 0.77508  - train: 0.80105 \nvar_57 Cum val: 0.77530  - train: 0.80168 \nvar_58 Cum val: 0.77643  - train: 0.80305 \nvar_59 Cum val: 0.77672  - train: 0.80378 \nvar_60 Cum val: 0.77692  - train: 0.80440 \nvar_61 Cum val: 0.77704  - train: 0.80486 \nvar_62 Cum val: 0.77734  - train: 0.80551 \nvar_63 Cum val: 0.77807  - train: 0.80662 \nvar_64 Cum val: 0.77831  - train: 0.80734 \nvar_65 Cum val: 0.77846  - train: 0.80783 \nvar_66 Cum val: 0.77929  - train: 0.80891 \nvar_67 Cum val: 0.78234  - train: 0.81183 \nvar_68 Cum val: 0.78256  - train: 0.81240 \nvar_69 Cum val: 0.78302  - train: 0.81309 \nvar_70 Cum val: 0.78479  - train: 0.81489 \nvar_71 Cum val: 0.78600  - train: 0.81625 \nvar_72 Cum val: 0.78623  - train: 0.81679 \nvar_73 Cum val: 0.78634  - train: 0.81722 \nvar_74 Cum val: 0.78706  - train: 0.81818 \nvar_75 Cum val: 0.79022  - train: 0.82101 \nvar_76 Cum val: 0.79602  - train: 0.82584 \nvar_77 Cum val: 0.79651  - train: 0.82662 \nvar_78 Cum val: 0.80046  - train: 0.83007 \nvar_79 Cum val: 0.80051  - train: 0.83039 \nvar_80 Cum val: 0.80608  - train: 0.83516 \nvar_81 Cum val: 0.81438  - train: 0.84214 \nvar_82 Cum val: 0.81588  - train: 0.84373 \nvar_83 Cum val: 0.81695  - train: 0.84487 \nvar_84 Cum val: 0.81711  - train: 0.84529 \nvar_85 Cum val: 0.81802  - train: 0.84633 \nvar_86 Cum val: 0.82100  - train: 0.84898 \nvar_87 Cum val: 0.82242  - train: 0.85036 \nvar_88 Cum val: 0.82297  - train: 0.85103 \nvar_89 Cum val: 0.82454  - train: 0.85262 \nvar_90 Cum val: 0.82584  - train: 0.85385 \nvar_91 Cum val: 0.82753  - train: 0.85554 \nvar_92 Cum val: 0.83062  - train: 0.85821 \nvar_93 Cum val: 0.83176  - train: 0.85938 \nvar_94 Cum val: 0.83441  - train: 0.86167 \nvar_95 Cum val: 0.83591  - train: 0.86310 \nvar_96 Cum val: 0.83586  - train: 0.86328 \nvar_97 Cum val: 0.83654  - train: 0.86410 \nvar_98 Cum val: 0.83645  - train: 0.86427 \nvar_99 Cum val: 0.84007  - train: 0.86735 \nvar_100 Cum val: 0.83999  - train: 0.86752 \nvar_101 Cum val: 0.83998  - train: 0.86776 \nvar_102 Cum val: 0.84081  - train: 0.86865 \nvar_103 Cum val: 0.84074  - train: 0.86883 \nvar_104 Cum val: 0.84125  - train: 0.86951 \nvar_105 Cum val: 0.84175  - train: 0.87019 \nvar_106 Cum val: 0.84290  - train: 0.87132 \nvar_107 Cum val: 0.84482  - train: 0.87307 \nvar_108 Cum val: 0.84740  - train: 0.87537 \nvar_109 Cum val: 0.85069  - train: 0.87810 \nvar_110 Cum val: 0.85443  - train: 0.88110 \nvar_111 Cum val: 0.85508  - train: 0.88189 \nvar_112 Cum val: 0.85580  - train: 0.88274 \nvar_113 Cum val: 0.85613  - train: 0.88322 \nvar_114 Cum val: 0.85688  - train: 0.88404 \nvar_115 Cum val: 0.85855  - train: 0.88550 \nvar_116 Cum val: 0.85895  - train: 0.88603 \nvar_117 Cum val: 0.85897  - train: 0.88626 \nvar_118 Cum val: 0.86050  - train: 0.88768 \nvar_119 Cum val: 0.86183  - train: 0.88886 \nvar_120 Cum val: 0.86201  - train: 0.88920 \nvar_121 Cum val: 0.86379  - train: 0.89084 \nvar_122 Cum val: 0.86560  - train: 0.89240 \nvar_123 Cum val: 0.86733  - train: 0.89391 \nvar_124 Cum val: 0.86734  - train: 0.89408 \nvar_125 Cum val: 0.86790  - train: 0.89469 \nvar_126 Cum val: 0.86791  - train: 0.89489 \nvar_127 Cum val: 0.86956  - train: 0.89635 \nvar_128 Cum val: 0.87026  - train: 0.89712 \nvar_129 Cum val: 0.87036  - train: 0.89741 \nvar_130 Cum val: 0.87143  - train: 0.89842 \nvar_131 Cum val: 0.87210  - train: 0.89912 \nvar_132 Cum val: 0.87268  - train: 0.89972 \nvar_133 Cum val: 0.87469  - train: 0.90148 \nvar_134 Cum val: 0.87503  - train: 0.90188 \nvar_135 Cum val: 0.87596  - train: 0.90278 \nvar_136 Cum val: 0.87594  - train: 0.90295 \nvar_137 Cum val: 0.87674  - train: 0.90374 \nvar_138 Cum val: 0.87701  - train: 0.90414 \nvar_139 Cum val: 0.88131  - train: 0.90747 \nvar_140 Cum val: 0.88139  - train: 0.90768 \nvar_141 Cum val: 0.88250  - train: 0.90861 \nvar_142 Cum val: 0.88277  - train: 0.90900 \nvar_143 Cum val: 0.88288  - train: 0.90921 \nvar_144 Cum val: 0.88325  - train: 0.90966 \nvar_145 Cum val: 0.88400  - train: 0.91039 \nvar_146 Cum val: 0.88670  - train: 0.91256 \nvar_147 Cum val: 0.88832  - train: 0.91386 \nvar_148 Cum val: 0.88982  - train: 0.91508 \nvar_149 Cum val: 0.89125  - train: 0.91632 \nvar_150 Cum val: 0.89182  - train: 0.91692 \nvar_151 Cum val: 0.89235  - train: 0.91742 \nvar_152 Cum val: 0.89248  - train: 0.91762 \nvar_153 Cum val: 0.89248  - train: 0.91777 \nvar_154 Cum val: 0.89376  - train: 0.91876 \nvar_155 Cum val: 0.89492  - train: 0.91972 \nvar_156 Cum val: 0.89518  - train: 0.92002 \nvar_157 Cum val: 0.89599  - train: 0.92071 \nvar_158 Cum val: 0.89596  - train: 0.92083 \nvar_159 Cum val: 0.89603  - train: 0.92099 \nvar_160 Cum val: 0.89608  - train: 0.92120 \nvar_161 Cum val: 0.89608  - train: 0.92137 \nvar_162 Cum val: 0.89667  - train: 0.92192 \nvar_163 Cum val: 0.89762  - train: 0.92271 \nvar_164 Cum val: 0.89910  - train: 0.92381 \nvar_165 Cum val: 0.90107  - train: 0.92537 \nvar_166 Cum val: 0.90283  - train: 0.92677 \nvar_167 Cum val: 0.90354  - train: 0.92739 \nvar_168 Cum val: 0.90378  - train: 0.92769 \nvar_169 Cum val: 0.90493  - train: 0.92863 \nvar_170 Cum val: 0.90628  - train: 0.92970 \nvar_171 Cum val: 0.90648  - train: 0.92995 \nvar_172 Cum val: 0.90748  - train: 0.93080 \nvar_173 Cum val: 0.90841  - train: 0.93159 \nvar_174 Cum val: 0.91033  - train: 0.93303 \nvar_175 Cum val: 0.91066  - train: 0.93337 \nvar_176 Cum val: 0.91065  - train: 0.93350 \nvar_177 Cum val: 0.91178  - train: 0.93439 \nvar_178 Cum val: 0.91200  - train: 0.93468 \nvar_179 Cum val: 0.91326  - train: 0.93559 \nvar_180 Cum val: 0.91393  - train: 0.93616 \nvar_181 Cum val: 0.91404  - train: 0.93635 \nvar_182 Cum val: 0.91406  - train: 0.93647 \nvar_183 Cum val: 0.91405  - train: 0.93659 \nvar_184 Cum val: 0.91550  - train: 0.93775 \nvar_185 Cum val: 0.91550  - train: 0.93784 \nvar_186 Cum val: 0.91581  - train: 0.93816 \nvar_187 Cum val: 0.91624  - train: 0.93859 \nvar_188 Cum val: 0.91705  - train: 0.93926 \nvar_189 Cum val: 0.91705  - train: 0.93937 \nvar_190 Cum val: 0.91856  - train: 0.94050 \nvar_191 Cum val: 0.91961  - train: 0.94135 \nvar_192 Cum val: 0.92030  - train: 0.94191 \nvar_193 Cum val: 0.92047  - train: 0.94212 \nvar_194 Cum val: 0.92063  - train: 0.94234 \nvar_195 Cum val: 0.92103  - train: 0.94271 \nvar_196 Cum val: 0.92131  - train: 0.94300 \nvar_197 Cum val: 0.92183  - train: 0.94348 \nvar_198 Cum val: 0.92325  - train: 0.94455 \nvar_199 Cum val: 0.92360  - train: 0.94491 \n","output_type":"stream"}],"execution_count":24},{"cell_type":"markdown","source":"# 评估集成的效果","metadata":{}},{"cell_type":"code","source":"preds_train_cum = (preds_train_cum - preds_train_cum.min()) / (preds_train_cum.max() - preds_train_cum.min())\npreds_oof_cum=(preds_oof_cum - preds_oof_cum.min()) / (preds_oof_cum.max() - preds_oof_cum.min())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T09:36:26.842511Z","iopub.execute_input":"2025-09-16T09:36:26.842843Z","iopub.status.idle":"2025-09-16T09:36:26.851248Z","shell.execute_reply.started":"2025-09-16T09:36:26.842807Z","shell.execute_reply":"2025-09-16T09:36:26.850307Z"}},"outputs":[],"execution_count":25},{"cell_type":"code","source":"# 1. 评估训练集\ntrain_auc = evaluate_binary_classifier(\n    y_true=target_train,  # 真实的训练标签\n    y_pred=(preds_train_cum > 0.5).astype(int),  # 二值化预测值（标签）\n    y_proba=preds_train_cum,  # 概率值\n    model_name=\"lightgbm_e\",  # 模型名称\n    title_suffix=\"(Training Set)\",  # 标题后缀\n    save_results=True\n)\n\n# 2. 评估验证集\nval_auc = evaluate_binary_classifier(\n    y_true=target_train,  # 真实的验证标签\n    y_pred=(preds_oof_cum > 0.5).astype(int),  # 二值化预测值（标签）\n    y_proba=preds_oof_cum,  # 概率值\n    model_name=\"lightgbm_e\",  # 模型名称\n    title_suffix=\"(Validation Set)\",  # 标题后缀\n    save_results=True\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T09:37:35.373875Z","iopub.execute_input":"2025-09-16T09:37:35.374210Z","iopub.status.idle":"2025-09-16T09:37:37.806656Z","shell.execute_reply.started":"2025-09-16T09:37:35.374186Z","shell.execute_reply":"2025-09-16T09:37:37.805754Z"}},"outputs":[{"name":"stdout","text":"\n=== (Training Set) ===\nAccuracy: 0.9242\nAUC: 0.9449\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.92      1.00      0.96    179902\n           1       0.96      0.26      0.41     20098\n\n    accuracy                           0.92    200000\n   macro avg       0.94      0.63      0.68    200000\nweighted avg       0.93      0.92      0.90    200000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n=== (Validation Set) ===\nAccuracy: 0.9222\nAUC: 0.9236\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.92      1.00      0.96    179902\n           1       0.93      0.25      0.39     20098\n\n    accuracy                           0.92    200000\n   macro avg       0.92      0.62      0.67    200000\nweighted avg       0.92      0.92      0.90    200000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":28},{"cell_type":"markdown","source":"# 用集成模型测试test","metadata":{}},{"cell_type":"code","source":"preds_test_cum = np.zeros(preds_test.shape[0])\nfor i in range(len(features)):\n    preds_test_cum += preds_test[:,i]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T09:38:01.164565Z","iopub.execute_input":"2025-09-16T09:38:01.164923Z","iopub.status.idle":"2025-09-16T09:38:01.450915Z","shell.execute_reply.started":"2025-09-16T09:38:01.164896Z","shell.execute_reply":"2025-09-16T09:38:01.449955Z"}},"outputs":[],"execution_count":29},{"cell_type":"markdown","source":"# 非集成训练，没有预处理的情况下在public上0.89694","metadata":{}},{"cell_type":"code","source":"def train_xgboost_cv(\n    X, y, n_splits=5, X_test=None,\n    xgb_params=None, early_stopping_rounds=50, verbose=100, random_state=42\n):\n    \"\"\"\n    X: pd.DataFrame\n    y: pd.Series or np.array (二分类0/1)\n    X_test: 可选 pd.DataFrame，用于生成平均的测试集预测\n    xgb_params: 可选，覆盖默认参数的dict\n    返回:\n      models: 每折模型列表\n      oof_pred: OOF概率预测 (与y同长度)\n      fold_aucs: 每折AUC\n      test_pred: 若提供X_test，则返回对test的平均概率预测；否则为None\n    \"\"\"\n    # 保证 y 是一维\n    if isinstance(y, (pd.Series, pd.DataFrame)):\n        y_array = np.asarray(y).ravel()\n    else:\n        y_array = np.array(y).ravel()\n\n    # 默认参数（与你原始代码风格一致） 'scale_pos_weight': 0.67459337735408}\n    default_params = dict(\n        objective='binary:logistic',\n        eval_metric='logloss',\n        n_estimators=10000,\n        learning_rate= 0.06013845295170628,#0.03\n        max_depth=1,# 3     \n        subsample=0.816954814787356,#0.8\n        min_child_weight=4,#7\n        colsample_bytree=0.9026884174677129,#0.7\n        reg_alpha=5.39358790419384e-05,#0.1\n        reg_lambda= 1.1777748682560547,#0.5\n        scale_pos_weight= 0.67459337735408*9,#4.5\n        gamma=3.1378647727234616,\n        max_delta_step=0,\n        random_state=random_state,\n        n_jobs=-1,\n        early_stopping_rounds=early_stopping_rounds\n    )\n    if xgb_params is not None:\n        default_params.update(xgb_params)\n\n    skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state)\n\n    oof_pred = np.zeros(len(X), dtype=float)\n    test_pred = np.zeros(len(X_test), dtype=float) if X_test is not None else None\n    models = []\n\n    for fold, (train_idx, val_idx) in enumerate(skf.split(X, y_array), 1):\n        print(f\"\\n===== Fold {fold}/{n_splits} =====\")\n\n        # 按行号切片（关键修复点）\n        X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]\n        y_train, y_val = y_array[train_idx], y_array[val_idx]\n\n        # 每折计算类别权重（可缓解不平衡）\n        # pos = np.sum(y_train == 1)\n        # neg = np.sum(y_train == 0)\n        # scale_pos_weight = (neg / pos) if pos > 0 else 1.0\n\n        params_fold = default_params.copy()\n        # params_fold['scale_pos_weight'] = params_fold.get('scale_pos_weight', scale_pos_weight)\n\n        model = xgb.XGBClassifier(**params_fold)\n\n        model.fit(\n            X_train, y_train,\n            eval_set=[(X_val, y_val)],\n            verbose=verbose,\n        )\n\n        # ====== 评估：调用封装的评估函数评估训练集 ======\n        y_pred = model.predict(X_train)\n        y_pred_proba = model.predict_proba(X_train)[:, 1]\n        \n        metrics = evaluate_binary_classifier(\n            y_true=y_train,\n            y_pred=y_pred,\n            y_proba=y_pred_proba,\n            model=model,\n            model_name=\"xgboost\",\n            label_names=('Class 0','Class 1'),\n            title_suffix=f'(Train Set)_{fold}',\n            save_results=True\n        )\n\n    \n        #  ====== 评估：调用封装的评估函数评估验证集 ======\n        y_pred = model.predict(X_val)\n        y_pred_proba = model.predict_proba(X_val)[:, 1]\n            \n        metrics = evaluate_binary_classifier(\n            y_true=y_val,\n            y_pred=y_pred,\n            y_proba=y_pred_proba,\n            model=model,\n            model_name=\"xgboost\",\n            label_names=('Class 0','Class 1'),\n            title_suffix=f'(Validation Set)_{fold}',\n            save_results=True\n        )\n        models.append(model)\n    return models\nxgboost_models=train_xgboost_cv(train_x_encoded,train_y)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-12T02:07:48.568266Z","iopub.execute_input":"2025-09-12T02:07:48.568575Z"}},"outputs":[{"name":"stdout","text":"\n===== Fold 1/5 =====\n[0]\tvalidation_0-logloss:0.55726\n[100]\tvalidation_0-logloss:0.51693\n[200]\tvalidation_0-logloss:0.49270\n[300]\tvalidation_0-logloss:0.47466\n[400]\tvalidation_0-logloss:0.45968\n[500]\tvalidation_0-logloss:0.44718\n[600]\tvalidation_0-logloss:0.43672\n[700]\tvalidation_0-logloss:0.42755\n[800]\tvalidation_0-logloss:0.41928\n[900]\tvalidation_0-logloss:0.41244\n[1000]\tvalidation_0-logloss:0.40607\n[1100]\tvalidation_0-logloss:0.40015\n[1200]\tvalidation_0-logloss:0.39475\n[1300]\tvalidation_0-logloss:0.39005\n[1400]\tvalidation_0-logloss:0.38519\n[1500]\tvalidation_0-logloss:0.38145\n[1600]\tvalidation_0-logloss:0.37747\n[1700]\tvalidation_0-logloss:0.37429\n[1800]\tvalidation_0-logloss:0.37088\n[1900]\tvalidation_0-logloss:0.36767\n[2000]\tvalidation_0-logloss:0.36508\n[2100]\tvalidation_0-logloss:0.36243\n[2200]\tvalidation_0-logloss:0.35987\n[2300]\tvalidation_0-logloss:0.35751\n[2400]\tvalidation_0-logloss:0.35555\n[2500]\tvalidation_0-logloss:0.35329\n[2600]\tvalidation_0-logloss:0.35148\n[2700]\tvalidation_0-logloss:0.34977\n[2800]\tvalidation_0-logloss:0.34807\n[2900]\tvalidation_0-logloss:0.34615\n[3000]\tvalidation_0-logloss:0.34491\n[3100]\tvalidation_0-logloss:0.34378\n[3200]\tvalidation_0-logloss:0.34169\n[3300]\tvalidation_0-logloss:0.34030\n[3400]\tvalidation_0-logloss:0.33915\n[3500]\tvalidation_0-logloss:0.33788\n[3600]\tvalidation_0-logloss:0.33673\n[3700]\tvalidation_0-logloss:0.33583\n[3800]\tvalidation_0-logloss:0.33470\n[3900]\tvalidation_0-logloss:0.33377\n[4000]\tvalidation_0-logloss:0.33281\n[4100]\tvalidation_0-logloss:0.33190\n[4200]\tvalidation_0-logloss:0.33112\n[4300]\tvalidation_0-logloss:0.33031\n[4400]\tvalidation_0-logloss:0.32955\n[4500]\tvalidation_0-logloss:0.32882\n[4600]\tvalidation_0-logloss:0.32798\n[4700]\tvalidation_0-logloss:0.32746\n[4800]\tvalidation_0-logloss:0.32672\n[4900]\tvalidation_0-logloss:0.32582\n[4932]\tvalidation_0-logloss:0.32578\n\n=== (Train Set)_1 ===\nAccuracy: 0.8803\nAUC: 0.9114\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.97      0.90      0.93    143921\n           1       0.44      0.74      0.56     16079\n\n    accuracy                           0.88    160000\n   macro avg       0.71      0.82      0.74    160000\nweighted avg       0.92      0.88      0.89    160000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAjYAAAGJCAYAAACZwnkIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABVaElEQVR4nO3deVQV9f8/8Odl37wX3FgSAVdEEBQVERdUAtc+mJkaKSouFbhR5pILmWZi5oqgLWKlaVoaqaG4koobiAIJabnrxQXhCso+vz/8MV+vLAKCl8bn45x7jvc9r5l5zSDwZLYrEwRBABEREZEEaGm6ASIiIqKawmBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYEOScOXKFchkMnz55ZeabqXGFBcXw9HREYsWLXqp6x09ejRsbW1f6jpryv3792FsbIw9e/ZouhX6/17m/ydbW1uMHj1afB8ZGQmZTIYzZ868lPV7enrC09PzpayLysdgQ/QS7NmzByEhIVWa56effsL169cRFBQEAJDJZJV6HT58uOY34AVduXIFY8aMQfPmzWFgYAALCwv06NED8+fPr9byytufDRo0wLhx4zB37txqLTc8PBxDhw5F06ZNIZPJ1H5JVsTW1rZSX5vIyMhq9VVb/vrrL4SEhODKlSuVqg8JCVHbHiMjIzRt2hSDBg3Chg0bkJeXp5G+Xqa63Bs9oaPpBoheBXv27EFYWFiVws3SpUsxfPhwKBQKAMAPP/ygNv37779HTExMqfE2bdq8UK9ff/01iouLX2gZT7t06RI6deoEQ0NDjB07Fra2trh9+zYSEhKwZMkSfPrpp1VeZkX787333sOqVatw8OBB9O7du0rLXbJkCR4+fIjOnTvj9u3blZ5vxYoVyM7OVuvvp59+wvLly9GwYUNxvGvXrlXqp7b99ddf+PTTT+Hp6Vmloyrh4eEwMTFBXl4ebt68ib1792Ls2LFYsWIFdu3aBWtra7G2Ov+fqttXWloatLRq9+/1inrbt29fra6bKofBhqgOOnv2LM6dO4dly5aJY++++65azYkTJxATE1Nq/FmPHj2CkZFRpdetq6tbtWafY/ny5cjOzkZiYiJsbGzUpt25c6dG1wU8CXaOjo6IjIyscrA5cuSIeLTGxMSk0vP5+vqqvVcqlfjpp5/g6+tbI6dhqvo1rG1vvfWWWmCbN28eNm3ahFGjRmHo0KE4ceKEOK2m/z89SxAE5ObmwtDQEPr6+rW6rufR09PT6PrpCZ6Kopfu8ePHsLe3h729PR4/fiyOZ2RkwNLSEl27dkVRUZE4vm3bNjg4OMDAwACOjo7YsWNHheftly9fDhsbGxgaGqJnz55ITk4uVXPw4EF0794dxsbGMDU1xf/+9z9cuHChVN3Zs2fRr18/yOVymJiYoE+fPmo/tAGgoKAAn376KVq2bAkDAwM0aNAA3bp1Q0xMDIAn1xiEhYUBUD+dVJGdO3dCT08PPXr0qLDuWZ6ennB0dER8fDx69OgBIyMjzJ49GwDw22+/YcCAAbCysoK+vj6aN2+Ozz77TG1fl/T79L59+vql9evXo3nz5tDX10enTp1w+vTp5/b0zz//oEmTJqVCDQA0bty41Ngff/whfm3q1auHAQMGICUlRa2/5+3P119/Hb///jsEQXhuf0+zsbF57temuiq7/yv6Gt6/fx8jR46EXC6Hqakp/P39ce7cuTJPc6WmpuKtt95C/fr1YWBggI4dOyIqKkqcHhkZiaFDhwIAevXq9cKnMv38/DBu3DicPHlS/L8PlH2NzZYtW+Dq6op69epBLpfDyckJK1eurFRftra2GDhwIPbu3YuOHTvC0NAQ69atE6eVdfrw0aNHmDhxIho0aAC5XI5Ro0bhwYMHajUymazMI4BPL/N5vZV1jc2dO3cQEBAAc3NzGBgYwNnZGRs3blSredHvMVLHIzb00hkaGmLjxo3w8PDAJ598gq+++goAEBgYiKysLERGRkJbWxsAsHv3bgwbNgxOTk5YvHgxHjx4gICAALz22mtlLvv777/Hw4cPERgYiNzcXKxcuRK9e/dGUlISzM3NAQD79+9Hv3790KxZM4SEhODx48dYvXo1PDw8kJCQIP4QTklJQffu3SGXy/Hxxx9DV1cX69atg6enJ44cOQI3NzcAT647WLx4McaNG4fOnTtDpVLhzJkzSEhIwOuvv46JEyfi1q1bZZ42Ks/x48fh6OhYrb9279+/j379+mH48OF49913xe2OjIyEiYkJgoODYWJigoMHD2LevHlQqVRYunTpc5e7efNmPHz4EBMnToRMJkNoaCjefPNN/PvvvxX2aWNjg/3791fq1NAPP/wAf39/+Pj4YMmSJXj06BHCw8PRrVs3nD17Fra2tpXan66urli+fDlSUlLg6Oj43G17Gaqy/8v6GhYXF2PQoEE4deoU3n//fdjb2+O3336Dv79/qXWlpKTAw8MDr732GmbOnAljY2P8/PPP8PX1xS+//ILBgwejR48emDx5MlatWoXZs2eLpzBf5FTmyJEjsX79euzbtw+vv/56mTUxMTEYMWIE+vTpgyVLlgAALly4gGPHjmHKlCmV6istLQ0jRozAxIkTMX78eLRu3brCvoKCgmBqaoqQkBCkpaUhPDwcV69exeHDh6sUZKu6zx4/fgxPT09cunQJQUFBsLOzw7Zt2zB69GhkZmZiypQpavXV/R6jZwhEGjJr1ixBS0tLiI2NFbZt2yYAEFasWKFW4+TkJDRp0kR4+PChOHb48GEBgGBjYyOOXb58WQAgGBoaCjdu3BDHT548KQAQpk2bJo65uLgIjRs3Fu7fvy+OnTt3TtDS0hJGjRoljvn6+gp6enrCP//8I47dunVLqFevntCjRw9xzNnZWRgwYECF2xoYGChU5dutSZMmwpAhQ6q8zJ49ewoAhIiIiFL1jx49KjU2ceJEwcjISMjNzRXH/P39y9y3DRo0EDIyMsTx3377TQAg/P777xX2mZycLBgaGgoABBcXF2HKlCnCzp07hZycHLW6hw8fCqampsL48ePVxpVKpaBQKNTGn7c/jx8/LgAQtm7dWmFvFTE2Nhb8/f2rNe/SpUsFAMLly5fFscru//K+hr/88kup75GioiKhd+/eAgBhw4YN4nifPn0EJycnteUWFxcLXbt2FVq2bCmOlXzfHTp0qFLbNX/+fAGAcPfu3TKnP3jwQAAgDB48WBx79v/TlClTBLlcLhQWFpa7nor6srGxEQAI0dHRZU57+mu2YcMGAYDg6uoq5Ofni+OhoaECAOG3334TxwAI8+fPf+4yK+qtZ8+eQs+ePcX3K1asEAAIP/74oziWn58vuLu7CyYmJoJKpRIE4cW/x0gdT0WRxoSEhKBt27bw9/fHBx98gJ49e2Ly5Mni9Fu3biEpKQmjRo1Su96hZ8+ecHJyKnOZvr6+akdzOnfuDDc3N/H239u3byMxMRGjR49G/fr1xbp27drh9ddfF+uKioqwb98++Pr6olmzZmKdpaUl3nnnHRw9ehQqlQoAYGpqipSUFFy8eLEG9soT9+/fh5mZWbXm1dfXx5gxY0qNGxoaiv9++PAh7t27h+7du+PRo0dITU197nKHDRum1lP37t0BAP/++2+F87Vt2xaJiYl49913ceXKFaxcuRK+vr4wNzfH119/LdbFxMQgMzMTI0aMwL1798SXtrY23NzccOjQoef2WKKkz3v37lV6ntpWlf1f1tcwOjoaurq6GD9+vDimpaWFwMBAtbqMjAwcPHgQb7/9triee/fu4f79+/Dx8cHFixdx8+bNWthCiN+nDx8+LLfG1NQUOTk5aqerqsrOzg4+Pj6Vrp8wYYLaEY/3338fOjo6tf5YgD179sDCwgIjRowQx3R1dTF58mRkZ2fjyJEjavXV/R4jdQw2pDF6enr47rvvcPnyZTx8+BAbNmxQOyx89epVAECLFi1KzVvWGAC0bNmy1FirVq3EWzNLllnWoes2bdrg3r17yMnJwd27d/Ho0aNy64qLi3H9+nUAwIIFC5CZmYlWrVrByckJ06dPx/nz55+z9c8nVPH6kBKvvfZamRcxpqSkYPDgwVAoFJDL5WjUqJF44XFWVtZzl9u0aVO19yU/gJ+9VqEsrVq1wg8//IB79+7h/Pnz+Pzzz6Gjo4MJEyZg//79ACAGw969e6NRo0Zqr3379lXpQuOSfVdb18tUR1X2f1lfw6tXr8LS0rLURcTPfi9cunQJgiBg7ty5pfZjye31tXHRNgDxzrB69eqVW/PBBx+gVatW6NevH5o0aYKxY8ciOjq6Suuxs7OrUv2zPxdMTExgaWlZ67dsX716FS1btix1p1bJqauSn0clXuR7jP4Pr7Ehjdq7dy8AIDc3FxcvXqzyD6y6oEePHvjnn3/w22+/Yd++ffjmm2+wfPlyREREYNy4cdVaZoMGDar9w+zpIwMlMjMz0bNnT8jlcixYsEB8nkxCQgJmzJhRqdtxS657elZVApi2tjacnJzg5OQEd3d39OrVC5s2bYKXl5fYww8//AALC4tS8+roVP7HVcm+e/rOHU2q6v4v62tYWSXL+uijj8o9qlHeHwYvquRC/YqW37hxYyQmJmLv3r34448/8Mcff2DDhg0YNWpUqYtqy/Mi+6eqnr24uzbVxPcYMdiQBp0/fx4LFizAmDFjkJiYiHHjxiEpKUl8bkvJXTSXLl0qNW9ZYwDKPB30999/ixcElywzLS2tVF1qaioaNmwIY2NjGBgYwMjIqNw6LS0ttWd11K9fH2PGjMGYMWOQnZ2NHj16ICQkRAw2VT1yYG9vj8uXL1dpnoocPnwY9+/fx6+//qp2p1VNrqOqOnbsCADi82KaN28O4MkvPi8vrwrnfd7+LNmuF32mT02pif1vY2ODQ4cOlbr1+9nvhZJTp7q6ui+8H6uq5GLu550m0tPTw6BBgzBo0CAUFxfjgw8+wLp16zB37ly0aNGixvu6ePEievXqJb7Pzs7G7du30b9/f3HMzMwMmZmZavPl5+eXep5RVXqzsbHB+fPnUVxcrHbUpuTUY1l3CtKL46ko0oiCggKMHj0aVlZWWLlyJSIjI5Geno5p06aJNVZWVnB0dMT333+v9vCzI0eOICkpqczl7ty5U+36gVOnTuHkyZPo168fgCfXyLi4uGDjxo1qP8SSk5Oxb98+8QedtrY2vL298dtvv6kdrk5PT8fmzZvRrVs3yOVyAE+uh3maiYkJWrRoofYUVmNjYwAo9YOzPO7u7khOTq6xJ7mW/CX49F9++fn5WLt2bY0svyJ//vknCgoKSo2XXN9QcrrPx8cHcrkcn3/+eZn1d+/eFf/9vP0ZHx8PhUKBtm3bvmj7NaIm9r+Pjw8KCgrUrksqLi4Wb30v0bhxY3h6emLdunVlPmSwKvuxKjZv3oxvvvkG7u7u6NOnT7l1z36/aGlpoV27dgAg/n+vyb4AYP369Wr/p8LDw1FYWCj+XACeBOvY2NhS8z17xKYqvfXv3x9KpRJbt24VxwoLC7F69WqYmJigZ8+e1dkceg4esSGNWLhwIRITE3HgwAHUq1cP7dq1w7x58zBnzhy89dZbYsD4/PPP8b///Q8eHh4YM2YMHjx4gDVr1sDR0VEt7JRo0aIFunXrhvfffx95eXlYsWIFGjRogI8//lisWbp0Kfr16wd3d3cEBASIt3srFAq151gsXLgQMTEx6NatGz744APo6Ohg3bp1yMvLQ2hoqFjn4OAAT09PuLq6on79+jhz5gy2b98ufhQC8OT2YwCYPHkyfHx8oK2tjeHDh5e7f/73v//hs88+w5EjR+Dt7V3t/Vyia9euMDMzg7+/PyZPngyZTIYffvjhpRziXrJkCeLj4/Hmm2+Kv8ASEhLw/fffo379+pg6dSoAQC6XIzw8HCNHjkSHDh0wfPhwNGrUCNeuXcPu3bvh4eGBNWvWAHj+/oyJicGgQYOq/Jf/77//jnPnzgF4Er7Pnz+PhQsXAgDeeOMNsf+qqon97+vri86dO+PDDz/EpUuXYG9vj6ioKGRkZABQP5IQFhaGbt26wcnJCePHj0ezZs2Qnp6OuLg43LhxQ9xGFxcXaGtrY8mSJcjKyoK+vj569+5d5vOFnrZ9+3aYmJggPz9ffPLwsWPH4OzsjG3btlU477hx45CRkYHevXujSZMmuHr1KlavXg0XFxfxCFt1+ypPfn4++vTpg7fffhtpaWlYu3YtunXrhjfeeEOtr/feew9DhgzB66+/jnPnzmHv3r2lTmdWpbcJEyZg3bp1GD16NOLj42Fra4vt27fj2LFjWLFiRYXXItEL0Nj9WPTKio+PF3R0dIRJkyapjRcWFgqdOnUSrKyshAcPHojjW7ZsEezt7QV9fX3B0dFRiIqKEoYMGSLY29uLNSW3Sy5dulRYtmyZYG1tLejr6wvdu3cXzp07V6qH/fv3Cx4eHoKhoaEgl8uFQYMGCX/99VepuoSEBMHHx0cwMTERjIyMhF69egnHjx9Xq1m4cKHQuXNnwdTUVDA0NBTs7e2FRYsWqd1eWlhYKEyaNElo1KiRIJPJKnXrd7t27YSAgIByp5d3u3fbtm3LrD927JjQpUsXwdDQULCyshI+/vhjYe/evaVuXS3vdu+lS5eWWibKuUX22fUGBgYKjo6OgkKhEHR1dYWmTZsKo0ePVruVvsShQ4cEHx8fQaFQCAYGBkLz5s2F0aNHC2fOnBFrKtqfFy5cEAAI+/fvr7Cvsvj7+wsAynw9fTv185R1u3dl939FX8O7d+8K77zzjlCvXj1BoVAIo0ePFo4dOyYAELZs2aJW+88//wijRo0SLCwsBF1dXeG1114TBg4cKGzfvl2t7uuvvxaaNWsmaGtrP/fW75LbvUteBgYGQpMmTYSBAwcK3333ndrt5SWe/f+0fft2wdvbW2jcuLGgp6cnNG3aVJg4caJw+/btSvVlY2NT7uMVyrvd+8iRI8KECRMEMzMzwcTERPDz81N73IMgPLl1fsaMGULDhg0FIyMjwcfHR7h06VKpZVbU27O3ewuCIKSnpwtjxowRGjZsKOjp6QlOTk6l/i+96PcYqZMJAq9Kov8eFxcXNGrU6IVuGa3rfvjhBwQGBuLatWswNTXVdDv/GVOnTkVsbCzi4+Pr1F1RtWXnzp0YPHgwjh49Cg8PD023Q6RxvMaG6rSCggIUFhaqjR0+fBjnzp0r9ehyqfHz80PTpk1LXUNB5bt//z6++eYbLFy4UJKh5umPIAGe3LGzevVqyOVydOjQQUNdEdUtPGJDddqVK1fg5eWFd999F1ZWVkhNTUVERAQUCgWSk5PRoEEDTbdIdVx+fr54HUp5FArFS72FuLrGjRuHx48fw93dHXl5efj1119x/PhxfP7555g1a5am2yOqE3jxMNVpZmZmcHV1xTfffIO7d+/C2NgYAwYMwBdffMFQQ5Vy/PhxtVt9y7Jhw4YyPzyxrunduzeWLVuGXbt2ITc3Fy1atMDq1avVLlQnetXxiA0RSdqDBw8QHx9fYU3btm1haWn5kjoiotrEYENERESSwYuHiYiISDJ4jc1LVFxcjFu3bqFevXqSvGODiIiotgiCgIcPH8LKyqrUB4s+jcHmJbp165ba5wsRERFR1Vy/fh1NmjQpdzqDzUtU8vjs69evi58zRERERM+nUqlgbW393I+iYLB5iUpOP8nlcgYbIiKianjepRy8eJiIiIgkg8GG6AWMHDkSn3/+uabbqDX37t1D48aNcePGDU23QkRUKQw2VONiY2MxaNAgWFlZQSaTYefOnaVqQkJCYG9vD2NjY5iZmcHLywsnT54Up1+5cgUBAQGws7ODoaEhmjdvjvnz5yM/P19tOT///DNcXFxgZGQEGxsbLF26tNS6Nm3aBGdnZxgZGcHS0hJjx47F/fv31WpWrFiB1q1bw9DQENbW1pg2bRpyc3Mr3M5z585hz549mDx5sjiWnZ2NoKAgNGnSBIaGhnBwcEBERITafBMnTkTz5s1haGiIRo0a4X//+x9SU1MrXNevv/4Kb29vNGjQADKZDImJiWrTMzIyMGnSJHEbmjZtismTJyMrK0utZtCgQTAxMUH79u1x9uxZtWUEBgZi2bJlamMNGzbEqFGjMH/+/Ar7IyKqKxhsqMbl5OTA2dm5wg9vbNWqFdasWYOkpCQcPXoUtra28Pb2xt27dwEAqampKC4uxrp165CSkoLly5cjIiICs2fPFpfxxx9/wM/PD++99x6Sk5Oxdu1aLF++HGvWrBFrjh07hlGjRiEgIAApKSnYtm0bTp06hfHjx4s1mzdvxsyZMzF//nxcuHAB3377LbZu3aq2rrKsXr0aQ4cOhYmJiTgWHByM6Oho/Pjjj7hw4QKmTp2KoKAgREVFiTWurq7YsGEDLly4gL1790IQBHh7e6OoqKjCfdqtWzcsWbKkzOm3bt3CrVu38OWXXyI5ORmRkZGIjo5GQECAWLNo0SI8fPgQCQkJ8PT0VNsHJ06cwMmTJzF16tRSyx4zZgw2bdr03M9bIiKqEwR6abKysgQAQlZWlqZbeWkACDt27HhuXcm+2b9/f7k1oaGhgp2dnfh+xIgRwltvvaVWs2rVKqFJkyZCcXGxIAiCsHTpUqFZs2alal577TXxfWBgoNC7d2+1muDgYMHDw6PcXgoLCwWFQiHs2rVLbbxt27bCggUL1MY6dOggfPLJJ+Uu69y5cwIA4dKlS+XWlLh8+bIAQDh79uxza3/++WdBT09PKCgoEARBEPr16yeEh4cLgiAIf/31l2BkZCQIgiDk5+cLzs7OwunTp8tdlp2dnfDNN988d51ERLWlsr9DecSGNC4/Px/r16+HQqGAs7NzuXVZWVmoX7+++D4vLw8GBgZqNYaGhrhx4wauXr0KAHB3d8f169exZ88eCIKA9PR0bN++Hf379xfn6dq1K+Lj43Hq1CkAwL///os9e/ao1Tzr/PnzyMrKQseOHdXGu3btiqioKNy8eROCIODQoUP4+++/4e3tXeZycnJysGHDBtjZ2dX4M46ysrIgl8uho/Pk5kdnZ2ccPHgQhYWF2Lt3L9q1awcACA0NhaenZ6lteVrnzp3x559/1mh/RES14qXELBIEgUdsnvX7778LxsbGgkwmE6ysrIRTp06Vu5yLFy8KcrlcWL9+vTi2bt06wcjISNi/f79QVFQkpKWlCfb29gIA4fjx42Ldzz//LJiYmAg6OjoCAGHQoEFCfn6+2vJXrlwp6OrqijXvvfdehdu1Y8cOQVtbWzwyVCI3N1cYNWqUAEDQ0dER9PT0hI0bN5aaPywsTDA2NhYACK1bt67U0RpBqPwRm7t37wpNmzYVZs+eLY5lZmYKI0aMEJo2bSr06NFDSElJEf7++2+hZcuWwr1794SJEycKdnZ2wtChQ4XMzEy15U2bNk3w9PSsVI9ERLWBR2yozuvVqxcSExNx/Phx9O3bF2+//Tbu3LlTqu7mzZvo27cvhg4dqnZdyPjx4xEUFISBAwdCT08PXbp0wfDhwwFAfNz2X3/9hSlTpmDevHmIj49HdHQ0rly5gvfee09czuHDh/H5559j7dq1SEhIwK+//ordu3fjs88+K7f3x48fQ19fv9TzFFavXo0TJ04gKioK8fHxWLZsGQIDA7F//361Oj8/P5w9exZHjhxBq1at8Pbbbz/3YuXKUqlUGDBgABwcHBASEiKOKxQKbN68GVevXsWRI0fg4OCAiRMnYunSpdi0aRP+/fdfpKWlwcjICAsWLFBbpqGhIR49elQj/RER1aqXFLRI4BGb52nRooXw+eefq43dvHlTaNmypTBy5EihqKiozPkKCwuFGzduCHl5ecKePXsEAMKdO3cEQRCEd999t9R1OH/++acAQLh165YgCILQrVs34aOPPlKr+eGHHwRDQ8Ny17lv3z4BgJCXlyeOPXr0SNDV1S113U1AQIDg4+NT7nbn5eUJRkZGwubNm8utKfG8IzYqlUpwd3cX+vTpIzx+/LjCZX333XfC4MGDBUEQhMGDBwthYWGCIAjCrl27hA4dOqjVvvfee8KAAQOe2x8RUW3hERv6zykuLkZeXp74/ubNm/D09BTvIirvQ8+0tbXx2muvQU9PDz/99BPc3d3RqFEjAMCjR49KzaetrQ3gyQeqVbbmWS4uLgCeHBEqUVBQgIKCgjKXVVxcXO52C4IAQRDUtr06VCoVvL29oaenh6ioqFLXHz3t7t27WLBgAVavXg0AKCoqQkFBgbgdz96hlZycjPbt279Qf0RELwM/UoFqXHZ2Ni5duiS+v3z5MhITE1G/fn00bdoUOTk5WLRoEd544w1YWlri3r17CAsLw82bNzF06FAA/xdqbGxs8OWXX4q3gQOAhYUFgCcPj9u+fTs8PT2Rm5uLDRs2YNu2bThy5IhYO2jQIIwfPx7h4eHw8fHB7du3MXXqVHTu3BlWVlZizVdffYX27dvDzc0Nly5dwty5czFo0CAx4DyrUaNG6NChA44ePSqGHLlcjp49e2L69OkwNDSEjY0Njhw5gu+//x5fffUVgCcXJm/duhXe3t5o1KgRbty4gS+++AKGhoZqFyvb29tj8eLFGDx4MIAnz6C5du0abt26BQBIS0sT94WFhYUYah49eoQff/wRKpUKKpVK7PXZ7Zg6dSo+/PBDvPbaawAADw8P/PDDD/D29sb69evh4eEh1j569Ajx8fGSfhAhEUmHTCjvT1KqcSqVCgqFQrxbpaZN+u5wjS+zOm6kJmLn0mmlxu27+sArYCYKC/Kxb/1CpP97AY+zs2BgLIe5XWt0HDgS5nb2AIALR6NxYEPZz2wJ+vYQAODxwyzsWjUbGTf/hSAAFs0d0OXNAFg0c1CrP3fgV6QcjoLqnhJ6hiZo0qY9ur41ASZmT47qFBcV4cyuH5F2Yh+yH9yDYT1T2Dm7o8ub46BvZFJq/SWSDv2G1OP7MPST/3teT05WBuJ++RrXU84gN0eFeg3M0bbHQLh4D4VMJkP2g3s4tPFL3Ln6N/JyHsJIbgarVu3Q6Y1RMLNoKi5nTUAv9BkzA2269a1wf3R6wx9u/xtd7j4HgFFLfoK8oYX4/mryKZzauQFvzQ6D7P8fXSrIy8WB777A1eTTMLezh/eEOTCSmwEA/j55AKeiNuLdRd+Xuy9eptVjPTXdAhFpQGV/hzLYvESvSrB5VRTm5+HHT0bBZ+I8WLZoq+l2as22RR+gXZ830bqLl6ZbAcBgQ/SqquzvUF5jQ1RNOnr68AqYhdzsrOcX/0c9fpiF5h26o5VbH023QkRUKbzGhugFNLF30XQLtcqwngId+o3QdBtERJXGIzZEREQkGQw2REREJBkMNkRERCQZDDZEREQkGQw2REREJBkMNkRERCQZDDZEREQkGQw2REREJBkMNkRERCQZDDZEREQkGQw2REREJBkMNkRERCQZDDZEREQkGQw2REREJBkMNkRERCQZDDZEREQkGQw2REREJBkMNkRERCQZDDZEREQkGQw2REREJBkMNkRERCQZGg02sbGxGDRoEKysrCCTybBz505xWkFBAWbMmAEnJycYGxvDysoKo0aNwq1bt9SWkZGRAT8/P8jlcpiamiIgIADZ2dlqNefPn0f37t1hYGAAa2trhIaGlupl27ZtsLe3h4GBAZycnLBnzx616YIgYN68ebC0tIShoSG8vLxw8eLFmtsZRERE9MI0GmxycnLg7OyMsLCwUtMePXqEhIQEzJ07FwkJCfj111+RlpaGN954Q63Oz88PKSkpiImJwa5duxAbG4sJEyaI01UqFby9vWFjY4P4+HgsXboUISEhWL9+vVhz/PhxjBgxAgEBATh79ix8fX3h6+uL5ORksSY0NBSrVq1CREQETp48CWNjY/j4+CA3N7cW9gwRERFVh0wQBEHTTQCATCbDjh074OvrW27N6dOn0blzZ1y9ehVNmzbFhQsX4ODggNOnT6Njx44AgOjoaPTv3x83btyAlZUVwsPD8cknn0CpVEJPTw8AMHPmTOzcuROpqakAgGHDhiEnJwe7du0S19WlSxe4uLggIiICgiDAysoKH374IT766CMAQFZWFszNzREZGYnhw4dXahtVKhUUCgWysrIgl8urs5sqNOm7wzW+TKK6ZvVYT023QEQaUNnfof+pa2yysrIgk8lgamoKAIiLi4OpqakYagDAy8sLWlpaOHnypFjTo0cPMdQAgI+PD9LS0vDgwQOxxsvLS21dPj4+iIuLAwBcvnwZSqVSrUahUMDNzU2sKUteXh5UKpXai4iIiGrPfybY5ObmYsaMGRgxYoSY1JRKJRo3bqxWp6Ojg/r160OpVIo15ubmajUl759X8/T0p+crq6YsixcvhkKhEF/W1tZV2mYiIiKqmv9EsCkoKMDbb78NQRAQHh6u6XYqbdasWcjKyhJf169f13RLREREkqaj6QaepyTUXL16FQcPHlQ7r2ZhYYE7d+6o1RcWFiIjIwMWFhZiTXp6ulpNyfvn1Tw9vWTM0tJSrcbFxaXc3vX19aGvr1+VzSUiIqIXUKeP2JSEmosXL2L//v1o0KCB2nR3d3dkZmYiPj5eHDt48CCKi4vh5uYm1sTGxqKgoECsiYmJQevWrWFmZibWHDhwQG3ZMTExcHd3BwDY2dnBwsJCrUalUuHkyZNiDREREWmeRoNNdnY2EhMTkZiYCODJRbqJiYm4du0aCgoK8NZbb+HMmTPYtGkTioqKoFQqoVQqkZ+fDwBo06YN+vbti/Hjx+PUqVM4duwYgoKCMHz4cFhZWQEA3nnnHejp6SEgIAApKSnYunUrVq5cieDgYLGPKVOmIDo6GsuWLUNqaipCQkJw5swZBAUFAXhyx9bUqVOxcOFCREVFISkpCaNGjYKVlVWFd3ERERHRy6XR270PHz6MXr16lRr39/dHSEgI7Ozsypzv0KFD8PT0BPDkAX1BQUH4/fffoaWlhSFDhmDVqlUwMTER68+fP4/AwECcPn0aDRs2xKRJkzBjxgy1ZW7btg1z5szBlStX0LJlS4SGhqJ///7idEEQMH/+fKxfvx6ZmZno1q0b1q5di1atWlV6e3m7N9GL4+3eRK+myv4OrTPPsXkVMNgQvTgGG6JXkySfY0NERERUEQYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMjQab2NhYDBo0CFZWVpDJZNi5c6fadEEQMG/ePFhaWsLQ0BBeXl64ePGiWk1GRgb8/Pwgl8thamqKgIAAZGdnq9WcP38e3bt3h4GBAaytrREaGlqql23btsHe3h4GBgZwcnLCnj17qtwLERERaZZGg01OTg6cnZ0RFhZW5vTQ0FCsWrUKEREROHnyJIyNjeHj44Pc3Fyxxs/PDykpKYiJicGuXbsQGxuLCRMmiNNVKhW8vb1hY2OD+Ph4LF26FCEhIVi/fr1Yc/z4cYwYMQIBAQE4e/YsfH194evri+Tk5Cr1QkRERJolEwRB0HQTACCTybBjxw74+voCeHKExMrKCh9++CE++ugjAEBWVhbMzc0RGRmJ4cOH48KFC3BwcMDp06fRsWNHAEB0dDT69++PGzduwMrKCuHh4fjkk0+gVCqhp6cHAJg5cyZ27tyJ1NRUAMCwYcOQk5ODXbt2if106dIFLi4uiIiIqFQvlaFSqaBQKJCVlQW5XF4j++1pk747XOPLJKprVo/11HQLRKQBlf0dWmevsbl8+TKUSiW8vLzEMYVCATc3N8TFxQEA4uLiYGpqKoYaAPDy8oKWlhZOnjwp1vTo0UMMNQDg4+ODtLQ0PHjwQKx5ej0lNSXrqUwvZcnLy4NKpVJ7ERERUe2ps8FGqVQCAMzNzdXGzc3NxWlKpRKNGzdWm66jo4P69eur1ZS1jKfXUV7N09Of10tZFi9eDIVCIb6sra2fs9VERET0IupssJGCWbNmISsrS3xdv35d0y0RERFJWp0NNhYWFgCA9PR0tfH09HRxmoWFBe7cuaM2vbCwEBkZGWo1ZS3j6XWUV/P09Of1UhZ9fX3I5XK1FxEREdWeOhts7OzsYGFhgQMHDohjKpUKJ0+ehLu7OwDA3d0dmZmZiI+PF2sOHjyI4uJiuLm5iTWxsbEoKCgQa2JiYtC6dWuYmZmJNU+vp6SmZD2V6YWIiIg0T6PBJjs7G4mJiUhMTATw5CLdxMREXLt2DTKZDFOnTsXChQsRFRWFpKQkjBo1ClZWVuKdU23atEHfvn0xfvx4nDp1CseOHUNQUBCGDx8OKysrAMA777wDPT09BAQEICUlBVu3bsXKlSsRHBws9jFlyhRER0dj2bJlSE1NRUhICM6cOYOgoCAAqFQvREREpHk6mlz5mTNn0KtXL/F9Sdjw9/dHZGQkPv74Y+Tk5GDChAnIzMxEt27dEB0dDQMDA3GeTZs2ISgoCH369IGWlhaGDBmCVatWidMVCgX27duHwMBAuLq6omHDhpg3b57as266du2KzZs3Y86cOZg9ezZatmyJnTt3wtHRUaypTC9ERESkWXXmOTavAj7HhujF8Tk2RK+m//xzbIiIiIiqisGGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSjTgeboqIizJ07F3Z2djA0NETz5s3x2WefQRAEsUYQBMybNw+WlpYwNDSEl5cXLl68qLacjIwM+Pn5QS6Xw9TUFAEBAcjOzlarOX/+PLp37w4DAwNYW1sjNDS0VD/btm2Dvb09DAwM4OTkhD179tTOhhMREVG11Olgs2TJEoSHh2PNmjW4cOEClixZgtDQUKxevVqsCQ0NxapVqxAREYGTJ0/C2NgYPj4+yM3NFWv8/PyQkpKCmJgY7Nq1C7GxsZgwYYI4XaVSwdvbGzY2NoiPj8fSpUsREhKC9evXizXHjx/HiBEjEBAQgLNnz8LX1xe+vr5ITk5+OTuDiIiInksmPH34o44ZOHAgzM3N8e2334pjQ4YMgaGhIX788UcIggArKyt8+OGH+OijjwAAWVlZMDc3R2RkJIYPH44LFy7AwcEBp0+fRseOHQEA0dHR6N+/P27cuAErKyuEh4fjk08+gVKphJ6eHgBg5syZ2LlzJ1JTUwEAw4YNQ05ODnbt2iX20qVLF7i4uCAiIqJS26NSqaBQKJCVlQW5XF4j++hpk747XOPLJKprVo/11HQLRKQBlf0dWqeP2HTt2hUHDhzA33//DQA4d+4cjh49in79+gEALl++DKVSCS8vL3EehUIBNzc3xMXFAQDi4uJgamoqhhoA8PLygpaWFk6ePCnW9OjRQww1AODj44O0tDQ8ePBArHl6PSU1JespS15eHlQqldqLiIiIao+OphuoyMyZM6FSqWBvbw9tbW0UFRVh0aJF8PPzAwAolUoAgLm5udp85ubm4jSlUonGjRurTdfR0UH9+vXVauzs7Eoto2SamZkZlEplhespy+LFi/Hpp59WdbOJiIiomur0EZuff/4ZmzZtwubNm5GQkICNGzfiyy+/xMaNGzXdWqXMmjULWVlZ4uv69euabomIiEjS6vQRm+nTp2PmzJkYPnw4AMDJyQlXr17F4sWL4e/vDwsLCwBAeno6LC0txfnS09Ph4uICALCwsMCdO3fUlltYWIiMjAxxfgsLC6Snp6vVlLx/Xk3J9LLo6+tDX1+/qptNRERE1VSnj9g8evQIWlrqLWpra6O4uBgAYGdnBwsLCxw4cECcrlKpcPLkSbi7uwMA3N3dkZmZifj4eLHm4MGDKC4uhpubm1gTGxuLgoICsSYmJgatW7eGmZmZWPP0ekpqStZDREREmleng82gQYOwaNEi7N69G1euXMGOHTvw1VdfYfDgwQAAmUyGqVOnYuHChYiKikJSUhJGjRoFKysr+Pr6AgDatGmDvn37Yvz48Th16hSOHTuGoKAgDB8+HFZWVgCAd955B3p6eggICEBKSgq2bt2KlStXIjg4WOxlypQpiI6OxrJly5CamoqQkBCcOXMGQUFBL32/EBERUdmqFWyaNWuG+/fvlxrPzMxEs2bNXripEqtXr8Zbb72FDz74AG3atMFHH32EiRMn4rPPPhNrPv74Y0yaNAkTJkxAp06dkJ2djejoaBgYGIg1mzZtgr29Pfr06YP+/fujW7duas+oUSgU2LdvHy5fvgxXV1d8+OGHmDdvntqzbrp27YrNmzdj/fr1cHZ2xvbt27Fz5044OjrW2PYSERHRi6nWc2y0tLTKvNsoPT0dTZs2RV5eXo01KCV8jg3Ri+NzbIheTZX9HVqli4ejoqLEf+/duxcKhUJ8X1RUhAMHDsDW1rbq3RIRERHVgCoFm5LrVmQyGfz9/dWm6erqwtbWFsuWLaux5oiIiIiqokrB5um7kU6fPo2GDRvWSlNERERE1VGt59hcvny5pvsgIiIiemHVfkDfgQMHcODAAdy5c0c8klPiu+++e+HGiIiIiKqqWsHm008/xYIFC9CxY0dYWlpCJpPVdF9EREREVVatYBMREYHIyEiMHDmypvshIiIiqrZqPaAvPz8fXbt2releiIiIiF5ItYLNuHHjsHnz5pruhYiIiOiFVOtUVG5uLtavX4/9+/ejXbt20NXVVZv+1Vdf1UhzRERERFVRrWBz/vx5uLi4AACSk5PVpvFCYiIiItKUagWbQ4cO1XQfRERERC+sWtfYEBEREdVF1Tpi06tXrwpPOR08eLDaDRERERFVV7WCTcn1NSUKCgqQmJiI5OTkUh+OSURERPSyVCvYLF++vMzxkJAQZGdnv1BDRERERNVVo9fYvPvuu/ycKCIiItKYGg02cXFxMDAwqMlFEhEREVVatU5Fvfnmm2rvBUHA7du3cebMGcydO7dGGiMiIiKqqmoFG4VCofZeS0sLrVu3xoIFC+Dt7V0jjRERERFVVbWCzYYNG2q6DyIiIqIXVq1gUyI+Ph4XLlwAALRt2xbt27evkaaIiIiIqqNawebOnTsYPnw4Dh8+DFNTUwBAZmYmevXqhS1btqBRo0Y12SMRERFRpVTrrqhJkybh4cOHSElJQUZGBjIyMpCcnAyVSoXJkyfXdI9ERERElVKtIzbR0dHYv38/2rRpI445ODggLCyMFw8TERGRxlTriE1xcTF0dXVLjevq6qK4uPiFmyIiIiKqjmoFm969e2PKlCm4deuWOHbz5k1MmzYNffr0qbHmiIiIiKqiWsFmzZo1UKlUsLW1RfPmzdG8eXPY2dlBpVJh9erVNd0jERERUaVU6xoba2trJCQkYP/+/UhNTQUAtGnTBl5eXjXaHBEREVFVVOmIzcGDB+Hg4ACVSgWZTIbXX38dkyZNwqRJk9CpUye0bdsWf/75Z231SkRERFShKgWbFStWYPz48ZDL5aWmKRQKTJw4EV999VWNNUdERERUFVUKNufOnUPfvn3Lne7t7Y34+PgXboqIiIioOqoUbNLT08u8zbuEjo4O7t69+8JNEREREVVHlYLNa6+9huTk5HKnnz9/HpaWli/c1NNu3ryJd999Fw0aNIChoSGcnJxw5swZcbogCJg3bx4sLS1haGgILy8vXLx4UW0ZGRkZ8PPzg1wuh6mpKQICApCdnV2q9+7du8PAwADW1tYIDQ0t1cu2bdtgb28PAwMDODk5Yc+ePTW6rURERPRiqhRs+vfvj7lz5yI3N7fUtMePH2P+/PkYOHBgjTX34MEDeHh4QFdXF3/88Qf++usvLFu2DGZmZmJNaGgoVq1ahYiICJw8eRLGxsbw8fFR69HPzw8pKSmIiYnBrl27EBsbiwkTJojTVSoVvL29YWNjg/j4eCxduhQhISFYv369WHP8+HGMGDECAQEBOHv2LHx9feHr61th0CMiIqKXSyYIglDZ4vT0dHTo0AHa2toICgpC69atAQCpqakICwtDUVEREhISYG5uXiPNzZw5E8eOHSv3TitBEGBlZYUPP/wQH330EQAgKysL5ubmiIyMxPDhw3HhwgU4ODjg9OnT6NixI4AnHwnRv39/3LhxA1ZWVggPD8cnn3wCpVIJPT09cd07d+4Ub2cfNmwYcnJysGvXLnH9Xbp0gYuLCyIiIiq1PSqVCgqFAllZWWVegP2iJn13uMaXSVTXrB7rqekWiEgDKvs7tEpHbMzNzXH8+HE4Ojpi1qxZGDx4MAYPHozZs2fD0dERR48erbFQAwBRUVHo2LEjhg4disaNG6N9+/b4+uuvxemXL1+GUqlUe36OQqGAm5sb4uLiAABxcXEwNTUVQw0AeHl5QUtLCydPnhRrevToIYYaAPDx8UFaWhoePHgg1jz7nB4fHx9xPWXJy8uDSqVSexEREVHtqfKTh21sbLBnzx7cu3cPJ0+exIkTJ3Dv3j3s2bMHdnZ2Ndrcv//+i/DwcLRs2RJ79+7F+++/j8mTJ2Pjxo0AAKVSCQClwpS5ubk4TalUonHjxmrTdXR0UL9+fbWaspbx9DrKqymZXpbFixdDoVCIL2tr6yptPxEREVVNtZ48DABmZmbo1KlTTfZSSnFxMTp27IjPP/8cANC+fXskJycjIiIC/v7+tbrumjBr1iwEBweL71UqFcMNERFRLarWZ0W9LJaWlnBwcFAba9OmDa5duwYAsLCwAPDk2p+npaeni9MsLCxw584dtemFhYXIyMhQqylrGU+vo7yakull0dfXh1wuV3sRERFR7anTwcbDwwNpaWlqY3///TdsbGwAAHZ2drCwsMCBAwfE6SqVCidPnoS7uzsAwN3dHZmZmWoPDjx48CCKi4vh5uYm1sTGxqKgoECsiYmJQevWrcU7sNzd3dXWU1JTsh4iIiLSvDodbKZNm4YTJ07g888/x6VLl7B582asX78egYGBAACZTIapU6di4cKFiIqKQlJSEkaNGgUrKyv4+voCeHKEp2/fvhg/fjxOnTqFY8eOISgoCMOHD4eVlRUA4J133oGenh4CAgKQkpKCrVu3YuXKlWqnkaZMmYLo6GgsW7YMqampCAkJwZkzZxAUFPTS9wsRERGVrdrX2LwMnTp1wo4dOzBr1iwsWLAAdnZ2WLFiBfz8/MSajz/+GDk5OZgwYQIyMzPRrVs3REdHw8DAQKzZtGkTgoKC0KdPH2hpaWHIkCFYtWqVOF2hUGDfvn0IDAyEq6srGjZsiHnz5qk966Zr167YvHkz5syZg9mzZ6Nly5bYuXMnHB0dX87OICIioueq0nNs6MXwOTZEL47PsSF6NdXKc2yIiIiI6jIGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikoz/VLD54osvIJPJMHXqVHEsNzcXgYGBaNCgAUxMTDBkyBCkp6erzXft2jUMGDAARkZGaNy4MaZPn47CwkK1msOHD6NDhw7Q19dHixYtEBkZWWr9YWFhsLW1hYGBAdzc3HDq1Kna2EwiIiKqpv9MsDl9+jTWrVuHdu3aqY1PmzYNv//+O7Zt24YjR47g1q1bePPNN8XpRUVFGDBgAPLz83H8+HFs3LgRkZGRmDdvnlhz+fJlDBgwAL169UJiYiKmTp2KcePGYe/evWLN1q1bERwcjPnz5yMhIQHOzs7w8fHBnTt3an/jiYiIqFJkgiAImm7iebKzs9GhQwesXbsWCxcuhIuLC1asWIGsrCw0atQImzdvxltvvQUASE1NRZs2bRAXF4cuXbrgjz/+wMCBA3Hr1i2Ym5sDACIiIjBjxgzcvXsXenp6mDFjBnbv3o3k5GRxncOHD0dmZiaio6MBAG5ubujUqRPWrFkDACguLoa1tTUmTZqEmTNnltl3Xl4e8vLyxPcqlQrW1tbIysqCXC6v8f006bvDNb5Morpm9VhPTbdARBqgUqmgUCie+zv0P3HEJjAwEAMGDICXl5faeHx8PAoKCtTG7e3t0bRpU8TFxQEA4uLi4OTkJIYaAPDx8YFKpUJKSopY8+yyfXx8xGXk5+cjPj5erUZLSwteXl5iTVkWL14MhUIhvqytrau5B4iIiKgy6nyw2bJlCxISErB48eJS05RKJfT09GBqaqo2bm5uDqVSKdY8HWpKppdMq6hGpVLh8ePHuHfvHoqKisqsKVlGWWbNmoWsrCzxdf369cptNBEREVWLjqYbqMj169cxZcoUxMTEwMDAQNPtVJm+vj709fU13QYREdEro04fsYmPj8edO3fQoUMH6OjoQEdHB0eOHMGqVaugo6MDc3Nz5OfnIzMzU22+9PR0WFhYAAAsLCxK3SVV8v55NXK5HIaGhmjYsCG0tbXLrClZBhEREWlenQ42ffr0QVJSEhITE8VXx44d4efnJ/5bV1cXBw4cEOdJS0vDtWvX4O7uDgBwd3dHUlKS2t1LMTExkMvlcHBwEGueXkZJTcky9PT04OrqqlZTXFyMAwcOiDVERESkeXX6VFS9evXg6OioNmZsbIwGDRqI4wEBAQgODkb9+vUhl8sxadIkuLu7o0uXLgAAb29vODg4YOTIkQgNDYVSqcScOXMQGBgoniZ67733sGbNGnz88ccYO3YsDh48iJ9//hm7d+8W1xscHAx/f3907NgRnTt3xooVK5CTk4MxY8a8pL1BREREz1Ong01lLF++HFpaWhgyZAjy8vLg4+ODtWvXitO1tbWxa9cuvP/++3B3d4exsTH8/f2xYMECscbOzg67d+/GtGnTsHLlSjRp0gTffPMNfHx8xJphw4bh7t27mDdvHpRKJVxcXBAdHV3qgmIiIiLSnP/Ec2ykorL34FcXn2NDrwI+x4bo1SSp59gQERERVQaDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0REUlCjx49sHnzZk23UWvy8/Nha2uLM2fOaLqVOo3BhojoFbd48WJ06tQJ9erVQ+PGjeHr64u0tDRx+pUrVyCTycp8bdu2Tay7du0aBgwYACMjIzRu3BjTp09HYWGhOP327dt455130KpVK2hpaWHq1Kmlevn666/RvXt3mJmZwczMDF5eXjh16tRztyEqKgrp6ekYPny4OLZ+/Xp4enpCLpdDJpMhMzOzzHl3794NNzc3GBoawszMDL6+vhWuKzs7G0FBQWjSpAkMDQ3h4OCAiIgItRqlUomRI0fCwsICxsbG6NChA3755Rdxel5eHkaOHAm5XI5WrVph//79avMvXboUkyZNUhvT09PDRx99hBkzZjx3f7zKGGyIiF5xR44cQWBgIE6cOIGYmBgUFBTA29sbOTk5AABra2vcvn1b7fXpp5/CxMQE/fr1AwAUFRVhwIAByM/Px/Hjx7Fx40ZERkZi3rx54nry8vLQqFEjzJkzB87OzmX2cvjwYYwYMQKHDh1CXFwcrK2t4e3tjZs3b1a4DatWrcKYMWOgpfV/v9YePXqEvn37Yvbs2eXO98svv2DkyJEYM2YMzp07h2PHjuGdd96pcF3BwcGIjo7Gjz/+iAsXLmDq1KkICgpCVFSUWDNq1CikpaUhKioKSUlJePPNN/H222/j7NmzAJ6Ervj4eMTFxWHChAl45513IAgCAODy5cv4+uuvsWjRolLr9vPzw9GjR5GSklJhj68ymVCyJ6nWVfYj16tr0neHa3yZRHXN6rGemm5B8u7evYvGjRvjyJEj6NGjR5k17du3R4cOHfDtt98CAP744w8MHDgQt27dgrm5OQAgIiICM2bMwN27d6Gnp6c2v6enJ1xcXLBixYoKeykqKoKZmRnWrFmDUaNGlduvubk5kpKS0LZt21LTDx8+jF69euHBgwcwNTUVxwsLC2Fra4tPP/0UAQEBFfbxNEdHRwwbNgxz584Vx1xdXdGvXz8sXLgQAGBiYoLw8HCMHDlSrGnQoAGWLFmCcePG4YMPPoBcLscXX3yBx48fw8jICHfu3EGjRo3Qt29fTJw4EYMHDy5z/b1794aHhwc+++yzSvcsBZX9HcojNkREpCYrKwsAUL9+/TKnx8fHIzExUS0MxMXFwcnJSQw1AODj4wOVSvVCRxcePXqEgoKCcnsBgKNHj8LIyAht2rSp0rITEhJw8+ZNaGlpoX379rC0tES/fv2QnJxc4Xxdu3ZFVFQUbt68CUEQcOjQIfz999/w9vZWq9m6dSsyMjJQXFyMLVu2IDc3F56engAAZ2dnHD16FI8fP8bevXthaWmJhg0bYtOmTTAwMCg31ABA586d8eeff1ZpW18lDDZERCQqLi7G1KlT4eHhAUdHxzJrvv32W7Rp0wZdu3YVx5RKpVqoASC+VyqV1e5nxowZsLKygpeXV7k1V69ehbm5udppqMr4999/AQAhISGYM2cOdu3aBTMzM3h6eiIjI6Pc+VavXg0HBwc0adIEenp66Nu3L8LCwtSObv38888oKChAgwYNoK+vj4kTJ2LHjh1o0aIFAGDs2LFwdnaGg4MDFi1ahJ9//hkPHjzAvHnzsHr1asyZMwctWrSAj49PqdNwVlZWuHr1apW29VWio+kGiIio7ggMDERycjKOHj1a5vTHjx9j8+bNaqdhassXX3yBLVu24PDhwzAwMCi37vHjxxVOL09xcTEA4JNPPsGQIUMAABs2bECTJk2wbds2TJw4scz5Vq9ejRMnTiAqKgo2NjaIjY1FYGCgWgCbO3cuMjMzsX//fjRs2BA7d+7E22+/jT///BNOTk7Q1dVFWFiY2nLHjBmDyZMn4+zZs9i5cyfOnTuH0NBQTJ48We3CY0NDQzx69KjK2/uqYLAhIiIAQFBQEHbt2oXY2Fg0adKkzJrt27fj0aNHpa53sbCwKHX3Unp6ujitqr788kt88cUX2L9/P9q1a1dhbcOGDfHgwYMqr8PS0hIA4ODgII7p6+ujWbNmuHbtWpnzPH78GLNnz8aOHTswYMAAAEC7du2QmJiIL7/8El5eXvjnn3+wZs0aJCcni9f8ODs7488//0RYWFipO6gA4NChQ0hJScE333yD6dOno3///jA2Nsbbb7+NNWvWqNVmZGSgUaNGVd7eVwVPRRERveIEQUBQUBB27NiBgwcPws7Ortzab7/9Fm+88UapX6zu7u5ISkrCnTt3xLGYmBjI5XK14FAZoaGh+OyzzxAdHY2OHTs+t759+/ZQKpVVDjeurq7Q19dXu7W9oKAAV65cgY2NTZnzFBQUoKCgoNRpL21tbfEIUMnRlIpqnpabm4vAwECsW7cO2traKCoqQkFBgbi+oqIitfrk5GS0b9++Stv6KmGwISJ6xQUGBuLHH3/E5s2bUa9ePSiVSiiVSjx+/Fit7tKlS4iNjcW4ceNKLcPb2xsODg4YOXIkzp07h71792LOnDkIDAyEvr6+WJeYmIjExERkZ2fj7t27SExMxF9//SVOX7JkCebOnYvvvvsOtra2Yi/Z2dnl9t++fXs0bNgQx44dUxtXKpVITEzEpUuXAABJSUlITEwUr5+Ry+V47733MH/+fOzbtw9paWl4//33AQBDhw4Vl2Nvb48dO3aI8/Ts2RPTp0/H4cOHcfnyZURGRuL7778XL/i1t7dHixYtMHHiRJw6dQr//PMPli1bhpiYmDKfkfPZZ5+hf//+Yljx8PDAr7/+ivPnz2PNmjXw8PBQq//zzz/VLlQmdbzd+yXi7d5EL+6/fLt37w8Wa7qFMh0KL/s5L/a9hsDS3lV8/8+JvUi/mAj3d6dDJiv9d3HuwwdIi/0NmbcuQ1tHFxatO6BZFx9oaWlXuC6DeqZwf/djAEDcj6HIfZhZqsa2Y2/YdSr/AuJ/4qKRm52Jtq//3wP6Lp/ejytnDla4XcVFRfj35F4o/z6L4sJCyM2t0dJjAIzr/9+F0IfCZ6vNk/foIf49sRcZNy6hMPcRDOqZwtKhM6zbeUAmkwEAHmXew78n9iJTeQVFBfkwVDRAU+fusGitfqQl+74SyXs3odPQSdDWfXJLvCAU4+8/f0f6xUQYmTaCg9cwGCkaAACylNdwfnckuvrPgraObrn7Q5MOrp1VK8ut7O9QBpuXiMGG6MUx2FBZ8h49xKktK9BpaBAM6plpup1ak7LvJxg3sIStq6emWymXpoMNT0UREdF/nr5RPdj3GlLm0R6pKC4qhHEDc1g7ezy/+BXGu6KIiEgSGtlV7SLl/xotbR3YuvbWdBt1Ho/YEBERkWQw2BAREZFkMNgQERGRZDDYEBERkWQw2BAREZFkMNgQERGRZDDYEBERkWQw2BAREZFk1Olgs3jxYnTq1An16tVD48aN4evrq/YprMD/fSpqgwYNYGJigiFDhiA9PV2t5tq1axgwYACMjIzQuHFjTJ8+HYWFhWo1hw8fRocOHaCvr48WLVogMjKyVD9hYWGwtbWFgYEB3NzccOrUqRrfZiIiIqq+Oh1sjhw5gsDAQJw4cQIxMTEoKCiAt7c3cnJyxJpp06bh999/x7Zt23DkyBHcunULb775pji9qKgIAwYMQH5+Po4fP46NGzciMjIS8+bNE2suX76MAQMGoFevXkhMTMTUqVMxbtw47N27V6zZunUrgoODMX/+fCQkJMDZ2Rk+Pj64c+fOy9kZRERE9Fz/qQ/BvHv3Lho3bowjR46gR48eyMrKQqNGjbB582a89dZbAIDU1FS0adMGcXFx6NKlC/744w8MHDgQt27dgrn5k09rjYiIwIwZM3D37l3o6elhxowZ2L17N5KTk8V1DR8+HJmZmYiOjgYAuLm5oVOnTlizZg0AoLi4GNbW1pg0aRJmzpxZqf75IZhEL44fgklUt/FDMKsgKysLAFC/fn0AQHx8PAoKCuDl9X8fZW9vb4+mTZsiLi4OABAXFwcnJycx1ACAj48PVCoVUlJSxJqnl1FSU7KM/Px8xMfHq9VoaWnBy8tLrClLXl4eVCqV2ouIiIhqz38m2BQXF2Pq1Knw8PCAo6MjAECpVEJPTw+mpqZqtebm5lAqlWLN06GmZHrJtIpqVCoVHj9+jHv37qGoqKjMmpJllGXx4sVQKBTiy9rauuobTkRERJX2nwk2gYGBSE5OxpYtWzTdSqXNmjULWVlZ4uv69euabomIiEjSdDTdQGUEBQVh165diI2NRZMmTcRxCwsL5OfnIzMzU+2oTXp6OiwsLMSaZ+9eKrlr6umaZ++kSk9Ph1wuh6GhIbS1taGtrV1mTckyyqKvrw99ff2qbzARERFVS50+YiMIAoKCgrBjxw4cPHgQdnZ2atNdXV2hq6uLAwcOiGNpaWm4du0a3N3dAQDu7u5ISkpSu3spJiYGcrkcDg4OYs3TyyipKVmGnp4eXF1d1WqKi4tx4MABsYaIiIg0r04fsQkMDMTmzZvx22+/oV69euL1LAqFAoaGhlAoFAgICEBwcDDq168PuVyOSZMmwd3dHV26dAEAeHt7w8HBASNHjkRoaCiUSiXmzJmDwMBA8WjKe++9hzVr1uDjjz/G2LFjcfDgQfz888/YvXu32EtwcDD8/f3RsWNHdO7cGStWrEBOTg7GjBnz8ncMERERlalOB5vw8HAAgKenp9r4hg0bMHr0aADA8uXLoaWlhSFDhiAvLw8+Pj5Yu3atWKutrY1du3bh/fffh7u7O4yNjeHv748FCxaINXZ2dti9ezemTZuGlStXokmTJvjmm2/g4+Mj1gwbNgx3797FvHnzoFQq4eLigujo6FIXFBMREZHm/KeeY/Nfx+fYEL04PseGqG7jc2yIiIiIagiDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0RERFJBoMNERERSQaDDREREUkGgw0RERFJBoNNFYWFhcHW1hYGBgZwc3PDqVOnNN0SERER/X8MNlWwdetWBAcHY/78+UhISICzszN8fHxw584dTbdGREREYLCpkq+++grjx4/HmDFj4ODggIiICBgZGeG7777TdGtEREQEQEfTDfxX5OfnIz4+HrNmzRLHtLS04OXlhbi4uDLnycvLQ15envg+KysLAKBSqWqnx8c5tbJcorqktr5/XobC/FxNt0BU62rre7RkuYIgVFjHYFNJ9+7dQ1FREczNzdXGzc3NkZqaWuY8ixcvxqefflpq3NraulZ6JHoVrA/SdAdEVBHFtwtqdfkPHz6EQqEodzqDTS2aNWsWgoODxffFxcXIyMhAgwYNIJPJNNgZ1QSVSgVra2tcv34dcrlc0+0Q0TP4PSotgiDg4cOHsLKyqrCOwaaSGjZsCG1tbaSnp6uNp6enw8LCosx59PX1oa+vrzZmampaWy2Shsjlcv7QJKrD+D0qHRUdqSnBi4crSU9PD66urjhw4IA4VlxcjAMHDsDd3V2DnREREVEJHrGpguDgYPj7+6Njx47o3LkzVqxYgZycHIwZM0bTrREREREYbKpk2LBhuHv3LubNmwelUgkXFxdER0eXuqCYXg36+vqYP39+qdONRFQ38Hv01SQTnnffFBEREdF/BK+xISIiIslgsCEiIiLJYLAhIiIiyWCwISIiIslgsCGqprCwMNja2sLAwABubm44deqUplsiIgCxsbEYNGgQrKysIJPJsHPnTk23RC8Rgw1RNWzduhXBwcGYP38+EhIS4OzsDB8fH9y5c0fTrRG98nJycuDs7IywsDBNt0IawNu9iarBzc0NnTp1wpo1awA8eQq1tbU1Jk2ahJkzZ2q4OyIqIZPJsGPHDvj6+mq6FXpJeMSGqIry8/MRHx8PLy8vcUxLSwteXl6Ii4vTYGdERMRgQ1RF9+7dQ1FRUaknTpubm0OpVGqoKyIiAhhsiIiISEIYbIiqqGHDhtDW1kZ6erraeHp6OiwsLDTUFRERAQw2RFWmp6cHV1dXHDhwQBwrLi7GgQMH4O7ursHOiIiIn+5NVA3BwcHw9/dHx44d0blzZ6xYsQI5OTkYM2aMplsjeuVlZ2fj0qVL4vvLly8jMTER9evXR9OmTTXYGb0MvN2bqJrWrFmDpUuXQqlUwsXFBatWrYKbm5um2yJ65R0+fBi9evUqNe7v74/IyMiX3xC9VAw2REREJBm8xoaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoheKTKZDDt37tR0G0RUSxhsiEhSlEolJk2ahGbNmkFfXx/W1tYYNGiQ2oeWEpF08UMwiUgyrly5Ag8PD5iammLp0qVwcnJCQUEB9u7di8DAQKSmpmq6RSKqZTxiQ0SS8cEHH0Amk+HUqVMYMmQIWrVqhbZt2yI4OBgnTpwoc54ZM2agVatWMDIyQrNmzTB37lwUFBSI08+dO4devXqhXr16kMvlcHV1xZkzZwAAV69exaBBg2BmZgZjY2O0bdsWe/bseSnbSkRl4xEbIpKEjIwMREdHY9GiRTA2Ni413dTUtMz56tWrh8jISFhZWSEpKQnjx49HvXr18PHHHwMA/Pz80L59e4SHh0NbWxuJiYnQ1dUFAAQGBiI/Px+xsbEwNjbGX3/9BRMTk1rbRiJ6PgYbIpKES5cuQRAE2NvbV2m+OXPmiP+2tbXFRx99hC1btojB5tq1a5g+fbq43JYtW4r1165dw5AhQ+Dk5AQAaNas2YtuBhG9IJ6KIiJJEAShWvNt3boVHh4esLCwgImJCebMmYNr166J04ODgzFu3Dh4eXnhiy++wD///CNOmzx5MhYuXAgPDw/Mnz8f58+ff+HtIKIXw2BDRJLQsmVLyGSyKl0gHBcXBz8/P/Tv3x+7du3C2bNn8cknnyA/P1+sCQkJQUpKCgYMGICDBw/CwcEBO3bsAACMGzcO//77L0aOHImkpCR07NgRq1evrvFtI6LKkwnV/TOHiKiO6devH5KSkpCWllbqOpvMzEyYmppCJpNhx44d8PX1xbJly7B27Vq1ozDjxo3D9u3bkZmZWeY6RowYgZycHERFRZWaNmvWLOzevZtHbog0iEdsiEgywsLCUFRUhM6dO+OXX37BxYsXceHCBaxatQru7u6l6lu2bIlr165hy5Yt+Oeff7Bq1SrxaAwAPH78GEFBQTh8+DCuXr2KY8eO4fTp02jTpg0AYOrUqdi7dy8uX76MhIQEHDp0SJxGRJrBi4eJSDKaNWuGhIQELFq0CB9++CFu376NRo0awdXVFeHh4aXq33jjDUybNg1BQUHIy8vDgAEDMHfuXISEhAAAtLW1cf/+fYwaNQrp6elo2LAh3nzzTXz66acAgKKiIgQGBuLGjRuQy+Xo27cvli9f/jI3mYiewVNRREREJBk8FUVERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREkvH/AD+n4yvmkYEvAAAAAElFTkSuQmCC\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n=== (Validation Set)_1 ===\nAccuracy: 0.8756\nAUC: 0.8989\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.97      0.89      0.93     35981\n           1       0.43      0.72      0.54      4019\n\n    accuracy                           0.88     40000\n   macro avg       0.70      0.81      0.73     40000\nweighted avg       0.91      0.88      0.89     40000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAApoAAAIjCAYAAACjybtCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABmXklEQVR4nO3deVxV1f7/8fcB5YAi4AjiPKVSpqam5JwmjmlaSVriPISW4pQ5m0nZ4JCmDbf0681SG7TUHHKsxCGVHHIWI1NwBAIVFPbvD3+c6wlUULZHOa9nj/24stfae3/2Ea4fPmvttS2GYRgCAAAAcpiLowMAAABA7kSiCQAAAFOQaAIAAMAUJJoAAAAwBYkmAAAATEGiCQAAAFOQaAIAAMAUJJoAAAAwBYkmAAAATEGiCadz5MgRtWjRQt7e3rJYLFq6dGmOnv/EiROyWCyaN29ejp73QdakSRM1adIkR8/5119/yd3dXb/++muOnvdG8+bNk8Vi0YkTJ2z7snovGzdulMVi0caNG3M0JovFogkTJuToOe83q1atkqenp86ePevoUADcJRJNOMSxY8fUr18/lS9fXu7u7vLy8lL9+vU1Y8YMXb582dRrh4SEaO/evXrzzTe1YMEC1a5d29Tr3Uvdu3eXxWKRl5dXpp/jkSNHZLFYZLFY9O6772b7/KdOndKECRMUGRmZA9HenUmTJqlu3bqqX7++rl69qiJFiqhBgwY37W8YhkqVKqXHHnvsHkZ5Z1auXHlfJpO//PKLWrVqpRIlSsjd3V2lS5dWu3bttHDhwjs634cffpjpL2QtW7ZUxYoVFR4enu1zJiYmavz48WrZsqUKFSrEL32AoxnAPbZ8+XLDw8PD8PHxMV555RXj448/NmbNmmUEBwcbefPmNfr06WPatS9dumRIMkaPHm3aNdLS0ozLly8b165dM+0aNxMSEmLkyZPHcHV1NRYtWpShffz48Ya7u7shyXjnnXeyff4dO3YYkozPP/88W8clJycbycnJ2b7ezZw5c8bImzevsXDhQtu+/v37GxaLxThx4kSmx2zcuNGQZLz33ntZvs7nn39uSDKioqJs+7J6Lxs2bDAkGRs2bMjy9dKFhoYaN/u/58uXLxtXr17N9jnv1uLFiw2LxWLUrFnTePvtt42PP/7YGDVqlFG/fn2jSZMmd3TOhx9+2GjcuHGmbR9++KGRL18+IyEhIVvnjIqKMiQZpUuXNpo0aXJH368Ack4eh2W4cEpRUVEKDg5WmTJltH79ehUvXtzWFhoaqqNHj2rFihWmXT99KM7Hx8e0a1gsFrm7u5t2/tuxWq2qX7++vvzySz3//PN2bQsXLlSbNm30zTff3JNYLl26pHz58snNzS1Hz/vf//5XefLkUbt27Wz7unbtqrlz5+rLL7/Ua6+9luGYhQsXysXFRcHBwXd17Zy+l+xy1PfWhAkTFBAQoK1bt2b4DM6cOZPj1+vUqZMGDRqkJUuWqGfPnlk+rnjx4jp9+rT8/Pz022+/qU6dOjkeG4CsY+gc99TUqVOVmJio//znP3ZJZrqKFSvq1VdftX197do1vfHGG6pQoYKsVqvKli2r119/XcnJyXbHlS1bVm3bttUvv/yixx9/XO7u7ipfvrz+7//+z9ZnwoQJKlOmjCRp+PDhslgsKlu2rKTrQ87pf77RhAkTZLFY7PatXbtWDRo0kI+Pjzw9PVW5cmW9/vrrtvabzdFcv369GjZsqPz588vHx0ft27fXgQMHMr3e0aNH1b17d/n4+Mjb21s9evTQpUuXbv7B/kuXLl30448/Ki4uzrZvx44dOnLkiLp06ZKh/4ULFzRs2DBVq1ZNnp6e8vLyUqtWrfT777/b+mzcuNH2j3aPHj1sQ/Dp99mkSRM98sgj2rlzpxo1aqR8+fLZPpd/z2sMCQmRu7t7hvsPCgpSwYIFderUqVve39KlS1W3bl15enra9tWvX19ly5bNdBj36tWr+vrrr9W0aVP5+/trz5496t69u23qhp+fn3r27Knz58/f8rqZ3YsknTx5Uh06dFD+/PlVrFgxDRkyJMP3qCT9/PPPeu6551S6dGlZrVaVKlVKQ4YMsZvm0L17d82ePVuSbJ/xjd+Dmc3R3L17t1q1aiUvLy95enqqWbNm2rp1q12f9Pmmv/76q8LCwlS0aFHlz59fzzzzTJbmQh47dkx16tTJNNEuVqyY3ddpaWmaPn26Hn74Ybm7u8vX11f9+vXTxYsXbX3Kli2r/fv3a9OmTbZ7vPFzLVasmB599FEtW7bstrHdyGq1ys/PL1vHADAPFU3cUz/88IPKly+vJ554Ikv9e/furfnz5+vZZ5/V0KFDtW3bNoWHh+vAgQP67rvv7PoePXpUzz77rHr16qWQkBB99tln6t69u2rVqqWHH35YHTt2lI+Pj4YMGaIXXnhBrVu3tktUsmL//v1q27atHn30UU2aNElWq1VHjx697QMpP/30k1q1aqXy5ctrwoQJunz5sj744APVr19fu3btypDkPv/88ypXrpzCw8O1a9cuffrppypWrJjefvvtLMXZsWNH9e/fX99++62tGrRw4UJVqVIl0zmKx48f19KlS/Xcc8+pXLlyio2N1UcffaTGjRvrjz/+kL+/v6pWrapJkyZp3Lhx6tu3rxo2bChJdn+X58+fV6tWrRQcHKwXX3xRvr6+mcY3Y8YMrV+/XiEhIYqIiJCrq6s++ugjrVmzRgsWLJC/v/9N7+3q1avasWOHBgwYYLffYrGoS5cumjJlivbv36+HH37Y1rZq1SpduHBBXbt2lXT9l4Xjx4+rR48e8vPz0/79+/Xxxx9r//792rp1a4ZfLm7l8uXLatasmaKjo/XKK6/I399fCxYs0Pr16zP0XbJkiS5duqQBAwaocOHC2r59uz744AOdPHlSS5YskST169dPp06d0tq1a7VgwYLbXn///v1q2LChvLy8NGLECOXNm1cfffSRmjRpok2bNqlu3bp2/QcNGqSCBQtq/PjxOnHihKZPn66BAwdq0aJFt7xOmTJltG7dOp08eVIlS5a8Zd9+/fpp3rx56tGjh1555RVFRUVp1qxZ2r17t3799VflzZtX06dP16BBg+Tp6anRo0dLUobvl1q1auX4w3oA7jFHj93DecTHxxuSjPbt22epf2RkpCHJ6N27t93+YcOGGZKM9evX2/aVKVPGkGRs3rzZtu/MmTOG1Wo1hg4datuXPn/r3/MTQ0JCjDJlymSIYfz48XZz5aZNm2ZIMs6ePXvTuNOvceO8sBo1ahjFihUzzp8/b9v3+++/Gy4uLka3bt0yXK9nz55253zmmWeMwoUL3/SaN95H/vz5DcMwjGeffdZo1qyZYRiGkZqaavj5+RkTJ07M9DO4cuWKkZqamuE+rFarMWnSJNu+W83RbNy4sSHJmDt3bqZt/56Lt3r1akOSMXnyZOP48eOGp6en0aFDh9ve49GjRw1JxgcffJChbf/+/YYkY9SoUXb7g4ODDXd3dyM+Pt4wjOtzdf/tyy+/zPA9lNkczX/fy/Tp0w1JxuLFi237kpKSjIoVK2aYo5nZdcPDww2LxWL8+eeftn23mqMpyRg/frzt6w4dOhhubm7GsWPHbPtOnTplFChQwGjUqFGGe2nevLmRlpZm2z9kyBDD1dXViIuLy/R66f7zn/8Ykgw3NzejadOmxtixY42ff/45w/fNzz//bEgyvvjiC7v9q1atyrD/VnM0DcMwpkyZYkgyYmNjbxnbzdzpnGIAOYehc9wzCQkJkqQCBQpkqf/KlSslSWFhYXb7hw4dKkkZ5nIGBATYqmySVLRoUVWuXFnHjx+/45j/LX1u57Jly5SWlpalY06fPq3IyEh1795dhQoVsu1/9NFH9dRTT9nu80b9+/e3+7phw4Y6f/687TPMii5dumjjxo2KiYnR+vXrFRMTk+mwuXR9uNHF5fr/HaSmpur8+fO2aQG7du3K8jWtVqt69OiRpb4tWrRQv379NGnSJHXs2FHu7u766KOPbntc+vB2wYIFM7QFBASoZs2a+uqrr2z7kpKS9P3336tt27by8vKSJHl4eNjar1y5onPnzqlevXqSlK37la5/nxYvXlzPPvusbV++fPnUt2/fDH1vvG5SUpLOnTunJ554QoZhaPfu3dm6rnT972rNmjXq0KGDypcvb9tfvHhxdenSRb/88kuG75m+ffvaVWwbNmyo1NRU/fnnn7e8Vs+ePbVq1So1adJEv/zyi9544w01bNhQlSpV0pYtW2z9lixZIm9vbz311FM6d+6cbatVq5Y8PT21YcOGLN9f+t/xuXPnsnwMgPsLiSbumfR/5P/5558s9f/zzz/l4uKiihUr2u338/OTj49Phn8YS5cuneEcBQsWtJsXdrc6d+6s+vXrq3fv3vL19VVwcLAWL158y6QzPc7KlStnaKtatarOnTunpKQku/3/vpf0f3Czcy+tW7dWgQIFtGjRIn3xxReqU6dOhs8yXVpamqZNm6ZKlSrJarWqSJEiKlq0qPbs2aP4+PgsX7NEiRLZeljm3XffVaFChRQZGamZM2dmmOt3K4ZhZLq/a9euioqKsiU/S5cu1aVLl2zD5tL1OamvvvqqfH195eHhoaJFi6pcuXKSlK37la7//VasWDHDcHtmf9/R0dG2Xzg8PT1VtGhRNW7c+I6uK11/uO3SpUs3/d5KS0vTX3/9Zbf/br63goKCtHr1asXFxWnz5s0KDQ3Vn3/+qbZt29oeCDpy5Iji4+NVrFgxFS1a1G5LTEzM1oND6X/H2ZnKAOD+whxN3DNeXl7y9/fXvn37snVcVv+RcXV1zXT/zRKSrFwjNTXV7msPDw9t3rxZGzZs0IoVK7Rq1SotWrRITz75pNasWXPTGLLrbu4lndVqVceOHTV//nwdP378lusyTpkyRWPHjlXPnj31xhtvqFChQnJxcdHgwYOzXLmV7Ct2WbF7925b4rF371698MILtz2mcOHCkm6eGL3wwgsaMWKEFi5cqCeeeEILFy5UwYIF1bp1a1uf559/Xlu2bNHw4cNVo0YNeXp6Ki0tTS1btszW/WZHamqqnnrqKV24cEEjR45UlSpVlD9/fv3999/q3r27adf9t5z43sqXL58aNmyohg0bqkiRIpo4caJ+/PFHhYSEKC0tTcWKFdMXX3yR6bFFixbN8nXS/46LFCmS5WMA3F9INHFPtW3bVh9//LEiIiIUGBh4y75lypRRWlqajhw5oqpVq9r2x8bGKi4uzvYEeU4oWLCg3RPa6TIbTnRxcVGzZs3UrFkzvf/++5oyZYpGjx6tDRs2qHnz5pnehyQdOnQoQ9vBgwdVpEgR5c+f/+5vIhNdunTRZ599dttlfdKfyP7Pf/5jtz8uLs7uH/mcrCwlJSWpR48eCggI0BNPPKGpU6fqmWeeue1yNKVLl5aHh4eioqIybff391fTpk21ZMkSjR07VmvXrlX37t1tldaLFy9q3bp1mjhxosaNG2c77siRI3d0H2XKlNG+fftkGIbd5/Pvv++9e/fq8OHDmj9/vrp162bbv3bt2gznzOrnXLRoUeXLl++m31suLi4qVapUVm/ljqS/8OD06dOSpAoVKuinn35S/fr1b/uLx+3uMyoqylZdB/BgYugc99SIESOUP39+9e7dW7GxsRnajx07phkzZkiSrQI1ffp0uz7vv/++JKlNmzY5FleFChUUHx+vPXv22PadPn06w5PtFy5cyHBsjRo1JCnT5Wyk6/PlatSoofnz59sls/v27dOaNWvsKm05rWnTpnrjjTc0a9asWy754urqmqGitWTJEv399992+9IT4syS8uwaOXKkoqOjNX/+fL3//vsqW7asQkJCbvo5psubN69q166t33777aZ9unbtqjNnzqhfv366evWq3bB5ekXv3/f77++zrGrdurVOnTqlr7/+2rbv0qVL+vjjj+36ZXZdwzBs3+83yurn7OrqqhYtWmjZsmV2r8mMjY3VwoUL1aBBA9uUlbu1bt26TPenzzFOH75//vnnlZqaqjfeeCND32vXrtndU/78+W95jzt37rztL6QA7m9UNHFPVahQQQsXLlTnzp1VtWpVdevWTY888ohSUlK0ZcsWLVmyRN27d5ckVa9eXSEhIfr4448VFxenxo0ba/v27Zo/f746dOigpk2b5lhcwcHBGjlypJ555hm98sorunTpkubMmaOHHnrI7uGQSZMmafPmzWrTpo3KlCmjM2fO6MMPP1TJkiVv+frDd955R61atVJgYKB69eplW97I29vb1FcNuri4aMyYMbft17ZtW02aNEk9evTQE088ob179+qLL76we8BEuv735+Pjo7lz56pAgQLKnz+/6tata5vfmFXr16/Xhx9+qPHjx9uWW/r888/VpEkTjR07VlOnTr3l8e3bt9fo0aOVkJCQaSLVqVMnvfzyy1q2bJlKlSqlRo0a2dq8vLzUqFEjTZ06VVevXlWJEiW0Zs2am1ZIb6dPnz6aNWuWunXrpp07d6p48eJasGCB8uXLZ9evSpUqqlChgoYNG6a///5bXl5e+uabbzKdAlCrVi1J0iuvvKKgoCC5urretCI9efJk29quL7/8svLkyaOPPvpIycnJt/0cs6N9+/YqV66c2rVrpwoVKigpKUk//fSTfvjhB9WpU8e2eH7jxo3Vr18/hYeHKzIyUi1atFDevHl15MgRLVmyRDNmzLA9OFWrVi3NmTNHkydPVsWKFVWsWDE9+eSTkq4vAr9nzx6FhoZmO9ZZs2YpLi7Oth7rDz/8oJMnT0q6vryTt7d3TnwkALLCQU+7w8kdPnzY6NOnj1G2bFnDzc3NKFCggFG/fn3jgw8+MK5cuWLrd/XqVWPixIlGuXLljLx58xqlSpUyRo0aZdfHMK4vb9SmTZsM1/n3UjQ3W97IMAxjzZo1xiOPPGK4ubkZlStXNv773/9mWN5o3bp1Rvv27Q1/f3/Dzc3N8Pf3N1544QXj8OHDGa7x7yVVfvrpJ6N+/fqGh4eH4eXlZbRr1874448/7PqkX+/fyydltsxOZm5c3uhmbra80dChQ43ixYsbHh4eRv369Y2IiIhMlyVatmyZERAQYOTJk8fuPhs3bmw8/PDDmV7zxvMkJCQYZcqUMR577LEMr1IcMmSI4eLiYkRERNzyHmJjY408efIYCxYsuGmf5557zpBkjBgxIkPbyZMnjWeeecbw8fExvL29jeeee844depUhqWDsrK8kWEYxp9//mk8/fTTRr58+YwiRYoYr776qm05nxuXN/rjjz+M5s2bG56enkaRIkWMPn36GL///nuG75dr164ZgwYNMooWLWpYLBa778F/x2gYhrFr1y4jKCjI8PT0NPLly2c0bdrU2LJli12f9HvZsWOH3f6svirzyy+/NIKDg40KFSoYHh4ehru7uxEQEGCMHj0609dEfvzxx0atWrUMDw8Po0CBAka1atWMESNGGKdOnbL1iYmJMdq0aWMUKFDAkGT3uc6ZM+eOXkFpGP9b7iyz7XY/QwBylsUwsjEDHADuE7169dLhw4f1888/OzoUmKBmzZpq0qSJpk2b5uhQANwFEk0AD6To6Gg99NBDWrdunerXr+/ocJCDVq1apWeffVbHjx/P1pJXAO4/JJoAgPteYmKiEhMTb9mnaNGiObbEGICcwcNAAID73rvvvquJEyfesk9UVJTKli17bwICkCVUNAEA973jx4/f9nWyDRo0kLu7+z2KCEBWkGgCAADAFCzYDgAAAFOQaAIAAMAUufJhII+aAx0dAgCTHN/4vqNDAGCS4t5uDru2mbnD5d2zTDv3/Y6KJgAAAEyRKyuaAAAA2WKh9mYGEk0AAACLxdER5Eqk7wAAADAFFU0AAACGzk3BpwoAAABTUNEEAABgjqYpqGgCAADAFFQ0AQAAmKNpCj5VAAAAmIKKJgAAAHM0TUGiCQAAwNC5KfhUAQAAYAoqmgAAAAydm4KKJgAAAExBRRMAAIA5mqbgUwUAAIApqGgCAAAwR9MUVDQBAABgCiqaAAAAzNE0BYkmAAAAQ+emIH0HAACAKahoAgAAMHRuCj5VAAAAmIKKJgAAABVNU/CpAgAAwBQkmgAAAC4W87ZsmDNnjh599FF5eXnJy8tLgYGB+vHHH23tV65cUWhoqAoXLixPT0916tRJsbGxdueIjo5WmzZtlC9fPhUrVkzDhw/XtWvX7Pps3LhRjz32mKxWqypWrKh58+ZliGX27NkqW7as3N3dVbduXW3fvj1b9yKRaAIAANw3SpYsqbfeeks7d+7Ub7/9pieffFLt27fX/v37JUlDhgzRDz/8oCVLlmjTpk06deqUOnbsaDs+NTVVbdq0UUpKirZs2aL58+dr3rx5GjdunK1PVFSU2rRpo6ZNmyoyMlKDBw9W7969tXr1alufRYsWKSwsTOPHj9euXbtUvXp1BQUF6cyZM9m6H4thGMZdfib3HY+aAx0dAgCTHN/4vqNDAGCS4t5uDru2x5Nvmnbuy+tH39XxhQoV0jvvvKNnn31WRYsW1cKFC/Xss89Kkg4ePKiqVasqIiJC9erV048//qi2bdvq1KlT8vX1lSTNnTtXI0eO1NmzZ+Xm5qaRI0dqxYoV2rdvn+0awcHBiouL06pVqyRJdevWVZ06dTRr1ixJUlpamkqVKqVBgwbptddey3LsVDQBAAAsFtO25ORkJSQk2G3Jycm3DSk1NVVfffWVkpKSFBgYqJ07d+rq1atq3ry5rU+VKlVUunRpRURESJIiIiJUrVo1W5IpSUFBQUpISLBVRSMiIuzOkd4n/RwpKSnauXOnXR8XFxc1b97c1ierSDQBAABMFB4eLm9vb7stPDz8pv337t0rT09PWa1W9e/fX999950CAgIUExMjNzc3+fj42PX39fVVTEyMJCkmJsYuyUxvT2+7VZ+EhARdvnxZ586dU2pqaqZ90s+RVSxvBAAAYOLyRqNGjVJYWJjdPqvVetP+lStXVmRkpOLj4/X1118rJCREmzZtMi0+M5FoAgAAmMhqtd4ysfw3Nzc3VaxYUZJUq1Yt7dixQzNmzFDnzp2VkpKiuLg4u6pmbGys/Pz8JEl+fn4Zng5Pfyr9xj7/flI9NjZWXl5e8vDwkKurq1xdXTPtk36OrGLoHAAAwMQ5mncrLS1NycnJqlWrlvLmzat169bZ2g4dOqTo6GgFBgZKkgIDA7V37167p8PXrl0rLy8vBQQE2PrceI70PunncHNzU61atez6pKWlad26dbY+WUVFEwAA4D4xatQotWrVSqVLl9Y///yjhQsXauPGjVq9erW8vb3Vq1cvhYWFqVChQvLy8tKgQYMUGBioevXqSZJatGihgIAAvfTSS5o6dapiYmI0ZswYhYaG2qqq/fv316xZszRixAj17NlT69ev1+LFi7VixQpbHGFhYQoJCVHt2rX1+OOPa/r06UpKSlKPHj2ydT8kmgAAAPfJKyjPnDmjbt266fTp0/L29tajjz6q1atX66mnnpIkTZs2TS4uLurUqZOSk5MVFBSkDz/80Ha8q6urli9frgEDBigwMFD58+dXSEiIJk2aZOtTrlw5rVixQkOGDNGMGTNUsmRJffrppwoKCrL16dy5s86ePatx48YpJiZGNWrU0KpVqzI8IHQ7rKMJ4IHCOppA7uXQdTRbvGPauS+vGW7aue93VDQBAAByYC4lMiLRBAAAuE+GznMbPlUAAACYgoomAAAAQ+emoKIJAAAAU1DRBAAAYI6mKfhUAQAAYAoqmgAAAMzRNAUVTQAAAJiCiiYAAABzNE1BogkAAECiaQo+VQAAAJiCiiYAAAAPA5mCiiYAAABMQUUTAACAOZqm4FMFAACAKahoAgAAMEfTFFQ0AQAAYAoqmgAAAMzRNAWJJgAAAEPnpiB9BwAAgCmoaAIAAKdnoaJpCiqaAAAAMAUVTQAA4PSoaJqDiiYAAABMQUUTAACAgqYpqGgCAADAFFQ0AQCA02OOpjlINAEAgNMj0TQHQ+cAAAAwBRVNAADg9KhomoOKJgAAAExBRRMAADg9KprmoKIJAAAAU1DRBAAAoKBpCiqaAAAAMAUVTQAA4PSYo2kOKpoAAAAwBRVNAADg9KhomoNEEwAAOD0STXMwdA4AAABTUNEEAABOj4qmOahoAgAAwBRUNAEAAChomoKKJgAAAExBRRMAADg95miag4omAAAATEFFEwAAOD0qmuYg0QQAAE6PRNMcDJ0DAADAFFQ0AQAAKGiagoomAAAATEFFEwAAOD3maJqDiiYAAABMQUUTAAA4PSqa5nBoopmSkqKlS5cqIiJCMTExkiQ/Pz898cQTat++vdzc3BwZHgAAAO6Cw4bOjx49qqpVqyokJES7d+9WWlqa0tLStHv3bnXr1k0PP/ywjh496qjwAACAE7FYLKZtzsxhFc0BAwaoWrVq2r17t7y8vOzaEhIS1K1bN4WGhmr16tUOihAAADgLZ08IzeKwRPPXX3/V9u3bMySZkuTl5aU33nhDdevWdUBkAAAAyAkOGzr38fHRiRMnbtp+4sQJ+fj43LN4AACAE7OYuDkxh1U0e/furW7dumns2LFq1qyZfH19JUmxsbFat26dJk+erEGDBjkqPAAAANwlhyWakyZNUv78+fXOO+9o6NChtrkRhmHIz89PI0eO1IgRIxwVHgAAcCLM0TSHQ5c3GjlypEaOHKmoqCi75Y3KlSvnyLAAAACQA+6LBdvLlStHcgkAAByGiqY5eAUlAADAfSI8PFx16tRRgQIFVKxYMXXo0EGHDh2y69OkSZMMa3X279/frk90dLTatGmjfPnyqVixYho+fLiuXbtm12fjxo167LHHZLVaVbFiRc2bNy9DPLNnz1bZsmXl7u6uunXravv27dm6HxJNAADg9O6XBds3bdqk0NBQbd26VWvXrtXVq1fVokULJSUl2fXr06ePTp8+bdumTp1qa0tNTVWbNm2UkpKiLVu2aP78+Zo3b57GjRtn6xMVFaU2bdqoadOmioyM1ODBg9W7d2+79csXLVqksLAwjR8/Xrt27VL16tUVFBSkM2fOZP1zNQzDyNYn8ADwqDnQ0SEAMMnxje87OgQAJinu7bhXT5cauMy0c/81q/0dH3v27FkVK1ZMmzZtUqNGjSRdr2jWqFFD06dPz/SYH3/8UW3bttWpU6dsq/rMnTtXI0eO1NmzZ+Xm5qaRI0dqxYoV2rdvn+244OBgxcXFadWqVZKkunXrqk6dOpo1a5YkKS0tTaVKldKgQYP02muvZSl+KpoAAAAmSk5OVkJCgt2WnJycpWPj4+MlSYUKFbLb/8UXX6hIkSJ65JFHNGrUKF26dMnWFhERoWrVqtmSTEkKCgpSQkKC9u/fb+vTvHlzu3MGBQUpIiJCkpSSkqKdO3fa9XFxcVHz5s1tfbLC4YnmqlWr9Msvv9i+nj17tmrUqKEuXbro4sWLDowMAAA4CzOHzsPDw+Xt7W23hYeH3zamtLQ0DR48WPXr19cjjzxi29+lSxf997//1YYNGzRq1CgtWLBAL774oq09JibGLsmUZPs6fZWfm/VJSEjQ5cuXde7cOaWmpmbaJ/0cWeHwp86HDx+ut99+W5K0d+9eDR06VGFhYdqwYYPCwsL0+eefOzhCAACAOzdq1CiFhYXZ7bNarbc9LjQ0VPv27bMryElS3759bX+uVq2aihcvrmbNmunYsWOqUKFCzgSdQxyeaEZFRSkgIECS9M0336ht27aaMmWKdu3apdatWzs4OgAA4AzMXN7IarVmKbG80cCBA7V8+XJt3rxZJUuWvGXfunXrSpKOHj2qChUqyM/PL8PT4bGxsZKur1ee/r/p+27s4+XlJQ8PD7m6usrV1TXTPunnyAqHD527ubnZ5hX89NNPatGihaTrcxESEhIcGRoAAMA9ZRiGBg4cqO+++07r16/P0jrjkZGRkqTixYtLkgIDA7V37167p8PXrl0rLy8vW3EvMDBQ69atszvP2rVrFRgYKOl6flarVi27PmlpaVq3bp2tT1Y4vKLZoEEDhYWFqX79+tq+fbsWLVokSTp8+PBtM3g8ePo810B9nm2oMv7XJzUfOB6jKR//qDW//qGCXvk0dkAbNatXRaX8CurcxUT9sHGPJn64XAmJV2zneG/Es6pXvbwerlhcB6NiVS/4LbtrNKxVSYNebKraD5eRl6e7jkaf1fT5P+mrH3+z9XmxXV19Muklu+OuJF9VwXpDTLx7wPks+3qRln27SDGnT0mSyparoJDe/VX3iYaSpPPnzmnuB+/pt20RunzpkkqVKasXe/RR4yefsp2jc/sgxf7/49P1CX1VXUN62742DEOLvpiv5d99rdiYU/L2Kaj2nTrrpZ59BWTF/bJge2hoqBYuXKhly5apQIECtvmQ3t7e8vDw0LFjx7Rw4UK1bt1ahQsX1p49ezRkyBA1atRIjz76qCSpRYsWCggI0EsvvaSpU6cqJiZGY8aMUWhoqK2y2r9/f82aNUsjRoxQz549tX79ei1evFgrVqywxRIWFqaQkBDVrl1bjz/+uKZPn66kpCT16NEjy/fj8ERz1qxZevnll/X1119rzpw5KlGihKTrj+a3bNnSwdEhp/0dG6exHyzT0eizssiiF9vV1ZJpfVUv+C1ZLBYVL+qtUdO+04HjMSpdvJA+GB2s4kW91WX4f+zO83/LtqpOtTJ6pFKJDNeoV72c9h35W+/PW6vY8/+odcNH9Okb3RSfeEU//vy/ZRzi/7ms6s9Msn2d+xb6AhyvqK+v+oYOVslSZWQYhlav+F6jh72iTxYsUbkKFRU+8XUl/vOPprz3gbx9fPTTqpWa+PowfTT/K1WqXNV2np79QtWm/bO2r/Plz2d3nQ/ee0s7tm3RgFeHqnyFSkpIiNc/CfH37D6BnDJnzhxJ15cwutHnn3+u7t27y83NTT/99JMt6StVqpQ6deqkMWPG2Pq6urpq+fLlGjBggAIDA5U/f36FhIRo0qT//ZtXrlw5rVixQkOGDNGMGTNUsmRJffrppwoKCrL16dy5s86ePatx48YpJiZGNWrU0KpVqzI8IHQrrKMJh/t749t6ffpSzV+acbmEjs1r6rM3u6nwE0OVmppm1za6X2u1a/pohopmZr6d2V9nzv+j/hO/kHS9ovnO8E4q3mhEztwE7hnW0XzwtWteX/0HDVWb9h3VsvHjChs5Vi1at7O1P928gfoOHKK2HTpJul7RfDb4RT33wkuZnu/PqOPq2aWTPv/qW5Uuw+uMH2SOXEez3OAVt+90h6KmtzHt3Pc7h8/R3LVrl/bu3Wv7etmyZerQoYNef/11paSkODAymM3FxaLngmopv4ebtu2JyrSPVwF3JSRdyZBkZpe3p4cuJlyy2+fpYdWhlZN05Mc3tHhaX1Utn/XJzQCyLzU1VevW/Kgrly/r4WrVJUmPPFpD69euUkJ8/PX5X2t+VEpKimrUqmN37ML5/9HTzRuo94vP6asFn9u9Sm/LzxvlX6KkIn7ZrOD2LdW5fZCmTh6vhHgqmsgGi4mbE3P40Hm/fv302muvqVq1ajp+/LiCg4P1zDPPaMmSJbp06dJNV71Pl5ycnGHRUyMtVRYXVxOjxt14uKK/Ns4fKne3PEq8nKzOQz/RweMZ1+Qq7JNfo/q00mffbLmr63V6qqZqPVxaAyd/adt35M8z6jfxC+07/Le8Cnho8EvNtGHeUNV69k39fSburq4HwN7xo4f1cq8XlZKSIg+PfHpj6nSVLX99CZbxU97VpNeH6+mnGsjVNY/c3d31xtTpKlmqtO34Ts93UaUqAfLy8tK+Pb/rkw+n6/y5swodcn1E4tTfJxUTc0ob163R6xPeVGpammZPm6rxr4Vp2pz/ZBoTgHvD4RXNw4cPq0aNGpKkJUuWqFGjRlq4cKHmzZunb7755rbHZ7YI6rXYnSZHjbtx+ESs6gaHq1G3d/XJkl/0yaSXVOVf1cQC+d313cwBOnD8tCZ/dOfDGY1qV9JHE1/Uy298qQM3JLPb9kRp4fLt2nP4b/2y86iCh32icxcT1evZ+nd8LQCZK1WmnD7979ea89kXat/peYVPHKMTx49Jkj6bO0uJif/ovVmf6KP5X+m5Lt004fVhOn70sO3457uGqGatOqpQqbLad3peL786XN8u/tI26mUYabqakqLXx7+pR2vWUs1adTRizETt3rld0X9mPloC/Nv98q7z3MbhiaZhGEpLuz4s+tNPP9nWzixVqpTOnTt32+NHjRql+Ph4uy2Pby1TY8bduXotVcf/OqfdB/7SuA++197Dfyv0hSa2ds98Vn0/+2X9c+mKOod9omvX7mzYvEGtivpmRn+NePdbLVy+/ZZ9r11L0++H/lKFUkXv6FoAbi5v3rwqWaq0Kld9WH1DB6tCpYf0zaL/6u+Tf+m7JV9qxJhJqvV4PVV8qLK69xmgylUD9N2Sr256vqoPV1Nq6jXFnP5bklS4SFG5uuZRqTJlbX3KlC0vSToTc9rUewNwaw5PNGvXrq3JkydrwYIF2rRpk9q0uT5hNioqKktPNVmtVnl5edltDJs/WFwsFlndrs/iKJDfXcvnDFTK1VQ9O/gjJadcu83RmWtYq5K+mzlAY2Ys02ff/nr7GFwseriiv2LOsXYrYDYjzVBKSoqSr1yWdP39yTdydXGVYdz8F8yjRw7KxcVFBQteXybtkUdrKjX1mv4++Zetz1/Rf0qSfP38czp85FJUNM3h8Dma06dPV9euXbV06VKNHj1aFStWlCR9/fXXeuKJJxwcHXLapEFPa/Wv+/XX6YsqkN9dnVvVVqPaldTu5Q+vJ5kfhsrD3U09Rs+XV353eeV3lySdvZiotLTrCySUL1VEnh5W+Rbxkoc1rx596PoSRweOx+jqtVQ1ql1J387sr9kLN2rput3yLVxAkpRyNdX2QNCovi21fc8JHfvrrHwKeGhISHOVLl5In393d/NBAdj7ePZ01Q1soGJ+xXX5UpJ+Wr1Skbt26J2Zc1W6bDmVKFVa74VP1IBXh8nL20e/bFqv37ZHKPz9WZKk/Xsi9cf+vapZ63Hly59P+/f+rtnT3tFTLduqgJe3JKnW4/X0UJWqmvrGWA0MG6m0tDRNf2eKatcNtKtyArj37tvlja5cuSJXV1flzZs328eyvNH9a874Lmr6eGX5FfFSfOIV7Tvyt977/Cet33ZQDWtV0ppPX830uMqtxyn69AVJ0upPXlWj2pVu2ufjiS/qpafrZWjf/NsRBfWZIUmaOrSj2jerId/CBXQx4bJ2H4jWxNnL9fuhkzl4tzADyxs9WKa+MU47f9umC+fOKr9nAZWvWElduvVU7brXCwkno//Ux7Ona+/vu3T50mWVKFlKnV/sblvu6PDBPzRt6puKPhGlq1dTVNy/hFq0aqfnunSTm9v/lsI5d/aMZr4brh3btsjd3UN1n2igl18dLi9vb4fcN+6MI5c3qjjsR9POffTdVqad+3533yaad4NEE8i9SDSB3ItEM/dx+NB5amqqpk2bpsWLFys6OjrD2pkXLlxwUGQAAMBZOPtcSrM4/GGgiRMn6v3331fnzp0VHx+vsLAwdezYUS4uLpowYYKjwwMAAE7AYjFvc2YOTzS/+OILffLJJxo6dKjy5MmjF154QZ9++qnGjRunrVu3Ojo8AAAA3CGHJ5oxMTGqVq2aJMnT01Px//+VYW3bttWKFea9dxQAACAdyxuZw+GJZsmSJXX69PUFdStUqKA1a9ZIknbs2CGr1erI0AAAAHAXHJ5oPvPMM1q3bp0kadCgQRo7dqwqVaqkbt26qWfPng6ODgAAOAPmaJrD4U+dv/XWW7Y/d+7cWaVLl1ZERIQqVaqkdu3aOTAyAAAA3A2HJ5r/FhgYqMDAQEeHAQAAnIiLi5OXHk3ikETz+++/z3Lfp59+2sRIAAAAYBaHJJodOnTIUj+LxaLU1FRzgwEAAE7P2edSmsUhiWZaWpojLgsAAJApZ1+GyCwOf+ocAAAAuZPDEs3169crICBACQkJGdri4+P18MMPa/PmzQ6IDAAAOBuWNzKHwxLN6dOnq0+fPvLy8srQ5u3trX79+mnatGkOiAwAAAA5wWGJ5u+//66WLVvetL1FixbauXPnPYwIAAA4K15BaQ6HJZqxsbHKmzfvTdvz5Mmjs2fP3sOIAAAAkJMclmiWKFFC+/btu2n7nj17VLx48XsYEQAAcFZUNM3hsESzdevWGjt2rK5cuZKh7fLlyxo/frzatm3rgMgAAACQExz2CsoxY8bo22+/1UMPPaSBAweqcuXKkqSDBw9q9uzZSk1N1ejRox0VHgAAcCJOXng0jcMSTV9fX23ZskUDBgzQqFGjZBiGpOul66CgIM2ePVu+vr6OCg8AADgRZx/iNovDEk1JKlOmjFauXKmLFy/q6NGjMgxDlSpVUsGCBR0ZFgAAAHKAQxPNdAULFlSdOnUcHQYAAHBSFDTNwSsoAQAAYIr7oqIJAADgSMzRNAcVTQAAAJiCiiYAAHB6FDTNQUUTAAAApqCiCQAAnB5zNM1BRRMAAACmoKIJAACcHgVNc5BoAgAAp8fQuTkYOgcAAIApqGgCAACnR0HTHFQ0AQAAYAoqmgAAwOkxR9McVDQBAABgCiqaAADA6VHQNAcVTQAAAJiCiiYAAHB6zNE0B4kmAABweuSZ5mDoHAAAAKagogkAAJweQ+fmoKIJAAAAU1DRBAAATo+KpjmoaAIAAMAUVDQBAIDTo6BpDiqaAAAAMAUVTQAA4PSYo2kOEk0AAOD0yDPNwdA5AAAATEFFEwAAOD2Gzs1BRRMAAACmoKIJAACcHgVNc1DRBAAAgCmoaAIAAKfnQknTFFQ0AQAAYAoSTQAA4PQsFvO27AgPD1edOnVUoEABFStWTB06dNChQ4fs+ly5ckWhoaEqXLiwPD091alTJ8XGxtr1iY6OVps2bZQvXz4VK1ZMw4cP17Vr1+z6bNy4UY899pisVqsqVqyoefPmZYhn9uzZKlu2rNzd3VW3bl1t3749W/dDogkAAJyexWIxbcuOTZs2KTQ0VFu3btXatWt19epVtWjRQklJSbY+Q4YM0Q8//KAlS5Zo06ZNOnXqlDp27GhrT01NVZs2bZSSkqItW7Zo/vz5mjdvnsaNG2frExUVpTZt2qhp06aKjIzU4MGD1bt3b61evdrWZ9GiRQoLC9P48eO1a9cuVa9eXUFBQTpz5kzWP1fDMIxsfQIPAI+aAx0dAgCTHN/4vqNDAGCS4t5uDrt20IfbTDv3971qKDk52W6f1WqV1Wq97bFnz55VsWLFtGnTJjVq1Ejx8fEqWrSoFi5cqGeffVaSdPDgQVWtWlURERGqV6+efvzxR7Vt21anTp2Sr6+vJGnu3LkaOXKkzp49Kzc3N40cOVIrVqzQvn37bNcKDg5WXFycVq1aJUmqW7eu6tSpo1mzZkmS0tLSVKpUKQ0aNEivvfZalu6diiYAAHB6LhbztvDwcHl7e9tt4eHhWYorPj5eklSoUCFJ0s6dO3X16lU1b97c1qdKlSoqXbq0IiIiJEkRERGqVq2aLcmUpKCgICUkJGj//v22PjeeI71P+jlSUlK0c+dOuz4uLi5q3ry5rU9W8NQ5AACAiUaNGqWwsDC7fVmpZqalpWnw4MGqX7++HnnkEUlSTEyM3Nzc5OPjY9fX19dXMTExtj43Jpnp7eltt+qTkJCgy5cv6+LFi0pNTc20z8GDB28bezoSTQAA4PTMfAVlVofJ/y00NFT79u3TL7/8YkJU9wZD5wAAAPeZgQMHavny5dqwYYNKlixp2+/n56eUlBTFxcXZ9Y+NjZWfn5+tz7+fQk//+nZ9vLy85OHhoSJFisjV1TXTPunnyAoSTQAA4PTul+WNDMPQwIED9d1332n9+vUqV66cXXutWrWUN29erVu3zrbv0KFDio6OVmBgoCQpMDBQe/futXs6fO3atfLy8lJAQICtz43nSO+Tfg43NzfVqlXLrk9aWprWrVtn65MVDJ0DAADcJ0JDQ7Vw4UItW7ZMBQoUsM2p9Pb2loeHh7y9vdWrVy+FhYWpUKFC8vLy0qBBgxQYGKh69epJklq0aKGAgAC99NJLmjp1qmJiYjRmzBiFhobahvD79++vWbNmacSIEerZs6fWr1+vxYsXa8WKFbZYwsLCFBISotq1a+vxxx/X9OnTlZSUpB49emT5fkg0AQCA07Po/ngF5Zw5cyRJTZo0sdv/+eefq3v37pKkadOmycXFRZ06dVJycrKCgoL04Ycf2vq6urpq+fLlGjBggAIDA5U/f36FhIRo0qRJtj7lypXTihUrNGTIEM2YMUMlS5bUp59+qqCgIFufzp076+zZsxo3bpxiYmJUo0YNrVq1KsMDQrfCOpoAHiisownkXo5cR/Ppj3eYdu7v+9Yx7dz3O+ZoAgAAwBQMnQMAAKdn5vJGzoyKJgAAAExBRRMAADg9CprmoKIJAAAAU1DRBAAATs+FkqYpqGgCAADAFFQ0AQCA06OgaQ4STQAA4PRY3sgcWUo09+zZk+UTPvroo3ccDAAAAHKPLCWaNWrUkMVi0c3eVpneZrFYlJqamqMBAgAAmI2CpjmylGhGRUWZHQcAAABymSwlmmXKlDE7DgAAAIdheSNz3NHyRgsWLFD9+vXl7++vP//8U5I0ffp0LVu2LEeDAwAAwIMr24nmnDlzFBYWptatWysuLs42J9PHx0fTp0/P6fgAAABMZzFxc2bZTjQ/+OADffLJJxo9erRcXV1t+2vXrq29e/fmaHAAAAB4cGV7Hc2oqCjVrFkzw36r1aqkpKQcCQoAAOBeYh1Nc2S7olmuXDlFRkZm2L9q1SpVrVo1J2ICAAC4p1ws5m3OLNsVzbCwMIWGhurKlSsyDEPbt2/Xl19+qfDwcH366admxAgAAIAHULYTzd69e8vDw0NjxozRpUuX1KVLF/n7+2vGjBkKDg42I0YAAABTMXRujjt613nXrl3VtWtXXbp0SYmJiSpWrFhOxwUAAIAH3B0lmpJ05swZHTp0SNL13wKKFi2aY0EBAADcSxQ0zZHth4H++ecfvfTSS/L391fjxo3VuHFj+fv768UXX1R8fLwZMQIAAOABlO1Es3fv3tq2bZtWrFihuLg4xcXFafny5frtt9/Ur18/M2IEAAAwlcViMW1zZtkeOl++fLlWr16tBg0a2PYFBQXpk08+UcuWLXM0OAAAADy4sp1oFi5cWN7e3hn2e3t7q2DBgjkSFAAAwL3k7OtdmiXbQ+djxoxRWFiYYmJibPtiYmI0fPhwjR07NkeDAwAAuBcYOjdHliqaNWvWtPugjhw5otKlS6t06dKSpOjoaFmtVp09e5Z5mgAAAJCUxUSzQ4cOJocBAADgOM5ddzRPlhLN8ePHmx0HAAAAcpk7XrAdAAAgt3Bx8rmUZsl2opmamqpp06Zp8eLFio6OVkpKil37hQsXciw4AAAAPLiy/dT5xIkT9f7776tz586Kj49XWFiYOnbsKBcXF02YMMGEEAEAAMxlsZi3ObNsJ5pffPGFPvnkEw0dOlR58uTRCy+8oE8//VTjxo3T1q1bzYgRAAAAD6BsJ5oxMTGqVq2aJMnT09P2fvO2bdtqxYoVORsdAADAPcA6mubIdqJZsmRJnT59WpJUoUIFrVmzRpK0Y8cOWa3WnI0OAAAAD6xsJ5rPPPOM1q1bJ0kaNGiQxo4dq0qVKqlbt27q2bNnjgcIAABgNuZomiPbT52/9dZbtj937txZZcqU0ZYtW1SpUiW1a9cuR4MDAAC4F1jeyBzZrmj+W7169RQWFqa6detqypQpORETAAAAcoG7TjTTnT59WmPHjs2p0wEAANwzDJ2bI8cSTQAAAOBGvIISAAA4PWdfhsgsVDQBAABgiixXNMPCwm7Zfvbs2bsOJqdc3DHL0SEAMElS8jVHhwAgF6LyZo4sJ5q7d+++bZ9GjRrdVTAAAADIPbKcaG7YsMHMOAAAAByGOZrm4GEgAADg9FzIM03BlAQAAACYgoomAABwelQ0zUFFEwAAAKagogkAAJweDwOZ444qmj///LNefPFFBQYG6u+//5YkLViwQL/88kuOBgcAAIAHV7YTzW+++UZBQUHy8PDQ7t27lZycLEmKj4/XlClTcjxAAAAAs7lYzNucWbYTzcmTJ2vu3Ln65JNPlDdvXtv++vXra9euXTkaHAAAAB5c2Z6jeejQoUzfAOTt7a24uLiciAkAAOCeYoqmObJd0fTz89PRo0cz7P/ll19Uvnz5HAkKAADgXnKxWEzbnFm2E80+ffro1Vdf1bZt22SxWHTq1Cl98cUXGjZsmAYMGGBGjAAAAHgAZXvo/LXXXlNaWpqaNWumS5cuqVGjRrJarRo2bJgGDRpkRowAAACmYmFxc1gMwzDu5MCUlBQdPXpUiYmJCggIkKenZ07HdseuXHN0BADMkpTMDziQWxXO77jlvV9fedi0c09p/ZBp577f3fHfqJubmwICAnIyFgAAAIdw8qmUpsl2otm0adNbrp6/fv36uwoIAAAAuUO2E80aNWrYfX316lVFRkZq3759CgkJyam4AAAA7hlnfzrcLNlONKdNm5bp/gkTJigxMfGuAwIAAEDukGMPWb344ov67LPPcup0AAAA94zFYt6WXZs3b1a7du3k7+8vi8WipUuX2rV3795dFovFbmvZsqVdnwsXLqhr167y8vKSj4+PevXqlaEguGfPHjVs2FDu7u4qVaqUpk6dmiGWJUuWqEqVKnJ3d1e1atW0cuXKbN1LjiWaERERcnd3z6nTAQAA3DP307vOk5KSVL16dc2ePfumfVq2bKnTp0/bti+//NKuvWvXrtq/f7/Wrl2r5cuXa/Pmzerbt6+tPSEhQS1atFCZMmW0c+dOvfPOO5owYYI+/vhjW58tW7bohRdeUK9evbR792516NBBHTp00L59+7J8L9le3qhjx452XxuGodOnT+u3337T2LFjNX78+OyczhQsbwTkXixvBORejlzeaMKaI+adu0WlOz7WYrHou+++U4cOHWz7unfvrri4uAyVznQHDhxQQECAduzYodq1a0uSVq1apdatW+vkyZPy9/fXnDlzNHr0aMXExMjNzU3S9bXSly5dqoMHD0qSOnfurKSkJC1fvtx27nr16qlGjRqaO3duluLPdkXT29vbbitUqJCaNGmilStX3hdJJgAAQHaZ+QrK5ORkJSQk2G3Jycl3Fe/GjRtVrFgxVa5cWQMGDND58+dtbREREfLx8bElmZLUvHlzubi4aNu2bbY+jRo1siWZkhQUFKRDhw7p4sWLtj7Nmze3u25QUJAiIiKyHGe2fnVITU1Vjx49VK1aNRUsWDA7hwIAADil8PBwTZw40W7f+PHjNWHChDs6X8uWLdWxY0eVK1dOx44d0+uvv65WrVopIiJCrq6uiomJUbFixeyOyZMnjwoVKqSYmBhJUkxMjMqVK2fXx9fX19ZWsGBBxcTE2Pbd2Cf9HFmRrUTT1dVVLVq00IEDB0g0AQBArmHm6kajRo1SWFiY3T6r1XrH5wsODrb9uVq1anr00UdVoUIFbdy4Uc2aNbvj85oh20PnjzzyiI4fP25GLAAAALmO1WqVl5eX3XY3iea/lS9fXkWKFNHRo0clSX5+fjpz5oxdn2vXrunChQvy8/Oz9YmNjbXrk/717fqkt2dFthPNyZMna9iwYVq+fLlOnz6dYc4BAADAg+Z+euo8u06ePKnz58+rePHikqTAwEDFxcVp586dtj7r169XWlqa6tata+uzefNmXb161dZn7dq1qly5sm3UOjAwUOvWrbO71tq1axUYGJjl2LKcaE6aNElJSUlq3bq1fv/9dz399NMqWbKkChYsqIIFC8rHx4fhdAAAgLuUmJioyMhIRUZGSpKioqIUGRmp6OhoJSYmavjw4dq6datOnDihdevWqX379qpYsaKCgoIkSVWrVlXLli3Vp08fbd++Xb/++qsGDhyo4OBg+fv7S5K6dOkiNzc39erVS/v379eiRYs0Y8YMuyH+V199VatWrdJ7772ngwcPasKECfrtt980cODALN9Llpc3cnV11enTp3XgwIFb9mvcuHGWL24WljcCci+WNwJyL0cubzRl3THTzv16swrZ6r9x40Y1bdo0w/6QkBDNmTNHHTp00O7duxUXFyd/f3+1aNFCb7zxht2DOxcuXNDAgQP1ww8/yMXFRZ06ddLMmTPl6elp67Nnzx6FhoZqx44dKlKkiAYNGqSRI0faXXPJkiUaM2aMTpw4oUqVKmnq1Klq3bp1lu8ly4mmi4tLpk8x3Y9INIHci0QTyL0cmWi+td68RPO1J7OXaOYm2ZqjaeGF8wAAAMiibP3q8NBDD9022bxw4cJdBQQAAHCv3YuHdpxRthLNiRMnytvb26xYAAAAkItkK9EMDg5+IOZoAgAAZAfTA82R5Tma/AUAAAAgO7Jc0cziw+kAAAAPHOZomiPLiWZaWpqZcQAAACCXcdyCVQAAAPcJZgiag0QTAAA4PRcyTVNka8F2AAAAIKuoaAIAAKfHw0DmoKIJAAAAU1DRBAAATo8pmuagogkAAABTUNEEAABOz0WUNM1ARRMAAACmoKIJAACcHnM0zUGiCQAAnB7LG5mDoXMAAACYgoomAABweryC0hxUNAEAAGAKKpoAAMDpUdA0BxVNAAAAmIKKJgAAcHrM0TQHFU0AAACYgoomAABwehQ0zUGiCQAAnB5DvObgcwUAAIApqGgCAACnZ2Hs3BRUNAEAAGAKKpoAAMDpUc80BxVNAAAAmIKKJgAAcHos2G4OKpoAAAAwBRVNAADg9KhnmoNEEwAAOD1Gzs3B0DkAAABMQUUTAAA4PRZsNwcVTQAAAJiCiiYAAHB6VN7MwecKAAAAU1DRBAAATo85muagogkAAABTUNEEAABOj3qmOahoAgAAwBRUNAEAgNNjjqY5SDQBAIDTY4jXHHyuAAAAMAUVTQAA4PQYOjcHFU0AAACYgoomAABwetQzzUFFEwAAAKagogkAAJweUzTNQUUTAAAApqCiCQAAnJ4LszRNQaIJAACcHkPn5mDoHAAAAKagogkAAJyehaFzU1DRBAAAgCmoaAIAAKfHHE1zUNEEAACAKahoAgAAp8fyRua4byuasbGxmjRpkqPDAAAAwB26bxPNmJgYTZw40dFhAAAAJ2CxmLc5M4clmnv27LnldujQIUeFBgAAnMz9lGhu3rxZ7dq1k7+/vywWi5YuXWrXbhiGxo0bp+LFi8vDw0PNmzfXkSNH7PpcuHBBXbt2lZeXl3x8fNSrVy8lJiba9dmzZ48aNmwod3d3lSpVSlOnTs0Qy5IlS1SlShW5u7urWrVqWrlyZbbuxWFzNGvUqCGLxSLDMDK0pe+3OPuvAQAAwOkkJSWpevXq6tmzpzp27JihferUqZo5c6bmz5+vcuXKaezYsQoKCtIff/whd3d3SVLXrl11+vRprV27VlevXlWPHj3Ut29fLVy4UJKUkJCgFi1aqHnz5po7d6727t2rnj17ysfHR3379pUkbdmyRS+88ILCw8PVtm1bLVy4UB06dNCuXbv0yCOPZOleLEZmmd49UKRIEU2dOlXNmjXLtH3//v1q166dUlNTs33uK9fuNjoA96ukZH7AgdyqcH7HPaO89sA5087dqHwBJScn2+2zWq2yWq23PdZisei7775Thw4dJF2vZvr7+2vo0KEaNmyYJCk+Pl6+vr6aN2+egoODdeDAAQUEBGjHjh2qXbu2JGnVqlVq3bq1Tp48KX9/f82ZM0ejR49WTEyM3NzcJEmvvfaali5dqoMHD0qSOnfurKSkJC1fvtwWT7169VSjRg3NnTs3S/fusKHzWrVq6dSpUypTpkymW4kSJTKtdgIAADxIwsPD5e3tbbeFh4ff0bmioqIUExOj5s2b2/Z5e3urbt26ioiIkCRFRETIx8fHlmRKUvPmzeXi4qJt27bZ+jRq1MiWZEpSUFCQDh06pIsXL9r63Hid9D7p18kKh/3q0L9/fyUlJd20vXTp0vr888/vYUQAAMBZuZg4W2/UqFEKCwuz25eVamZmYmJiJEm+vr52+319fW1tMTExKlasmF17njx5VKhQIbs+5cqVy3CO9LaCBQsqJibmltfJCoclms8888wt2wsWLKiQkJB7FA0AAIA5sjpMnhvdt8sbAQAA3CsWE//LSX5+fpKurzd+o9jYWFubn5+fzpw5Y9d+7do1Xbhwwa5PZue48Ro365PenhUkmgAAAA+IcuXKyc/PT+vWrbPtS0hI0LZt2xQYGChJCgwMVFxcnHbu3Gnrs379eqWlpalu3bq2Pps3b9bVq1dtfdauXavKlSurYMGCtj43Xie9T/p1soJEEwAAOL37aR3NxMRERUZGKjIyUtL1B4AiIyMVHR0ti8WiwYMHa/Lkyfr++++1d+9edevWTf7+/rYn06tWraqWLVuqT58+2r59u3799VcNHDhQwcHB8vf3lyR16dJFbm5u6tWrl/bv369FixZpxowZdnNJX331Va1atUrvvfeeDh48qAkTJui3337TwIEDs/65Omp5IzOxvBGQe7G8EZB7OXJ5o42HLph27iaVC2Wr/8aNG9W0adMM+0NCQjRv3jwZhqHx48fr448/VlxcnBo0aKAPP/xQDz30kK3vhQsXNHDgQP3www9ycXFRp06dNHPmTHl6etr67NmzR6GhodqxY4eKFCmiQYMGaeTIkXbXXLJkicaMGaMTJ06oUqVKmjp1qlq3bp3leyHRBPBAIdEEci8SzdzH4UPnq1at0i+//GL7evbs2apRo4a6dOliW8cJAADATC4W8zZn5vBEc/jw4UpISJAk7d27V0OHDlXr1q0VFRWVYc0pAAAAPDgcV6P+/6KiohQQECBJ+uabb9S2bVtNmTJFu3btytYcAAAAgDuV08sQ4TqHVzTd3Nx06dIlSdJPP/2kFi1aSJIKFSpkq3QCAADgwePwimaDBg0UFham+vXra/v27Vq0aJEk6fDhwypZsqSDo8O9sPO3HZr32X904I99Onv2rKbNnK0nm/3v3ao/rV2jJYu/0oH9+xUfH6dFXy9VlapVbe3xcXH6cPYHitjyi2JOn1bBgoXUtFlzhQ56VQUKFJAkHTp4UJ99+rF2796puIsX5V+ihJ57PlhdX+LtU4CZ/u+zT7Rx/VpFn4iSm9Vd1arX0MuvhKlM2f+9+u78ubOaNf097di2RZeSLql02bIK6dVXTZu1sPU5dOAPfTjzfR3Yv08uri5q8uRTemXoCOXLl1+StOL77/TmhDGZxrD8p80qVKiwuTeKB96dLEOE23N4RXPWrFnKkyePvv76a82ZM0clSpSQJP34449q2bKlg6PDvXD58iVVrlxZo8aMv2l7zZqPaXDYsEzbz5w9o7Nnzihs2Eh9s3S5Jr0Zrl9/+VkTxo629fnjj30qVLiQprz1jr5dtkK9+/bXzOnv68sv/mvKPQG4bvfOHer0/Av6eP6XmjHnE127dk2DX+6jy5cv2fpMGve6ov+M0tRps7Rg8Xdq/GRzjR05VIcOHpAknT17Rq8M6KWSpUrrk//7Uu/P+khRx49q8vj//Yw3b9FKP6zZaLfVDayvmrXqkGQCDsTyRrivVH+4coaKZrq//z6p1i2aZahoZmbN6h/1+sjh2vpbpPLkybxwP+WNiTp+/Jg+/fz/ciR23Bssb/Rgu3jxgto0a6jZn8xXzVq1JUnN6tfWsFHj1Krt07Z+LZs+oZdfCdPTzzyrpd8s1idzZumHNRvl4nK9PnLsyGG91PkZLV66UiVLl8n0Ou2DmmrUuDfszov7myOXN/r1iHkr3dSvVNC0c9/vHF7R3LVrl/bu3Wv7etmyZerQoYNef/11paSkODAyPMgS/0mUp6fnTZNMSfon8R95e/vcu6AAKOmffyRJXt7etn2PVK+pdWtWKSE+TmlpaVq7eqVSklP0WK06kqSrV68qb968tiRTkqxWqyTp98hdmV7nx+Xfy93dQ082b5FpO/BvLhaLaZszc3ii2a9fPx0+fFiSdPz4cQUHBytfvnxasmSJRowYcdvjk5OTlZCQYLclJyebHTbuYxcvXtDHcz9Up+c637RP5O5dWrPqR3V67vl7GBng3NLS0jT93bf1aI2aqlCxkm3/5Lff07VrV9WyaX01rldTU9+cqPD3ZtgqlbXq1NX58+f0xfzPdPVqihIS4vXhB9MkSefPncv0WsuXfqOnWrWW1d3d/BsDcFMOTzQPHz6sGjVqSLr+mqNGjRpp4cKFmjdvnr755pvbHh8eHi5vb2+77Z23w02OGverxMREDRzQT+UrVFD/lzN/F+uRI4c1eNDL6jcgVE/Ub3CPIwSc13tvTdbxY0c0Kfxdu/2ffPiBEhP/0cw5/9Fn/12k4K4hGjtyqI4duV6EKF+hosZOfFNf/neennyitto91Vj+/iVVqHBhWTJZDXvv75E6EXVc7dp3uif3hdzBYuLmzBz+1LlhGEpLS5N0fXmjtm3bSpJKlSqlczf5TfVGo0aNyrCwu+FqzflAcd9LSkrUy/16K3/+/Jo2c7by5s2boc+xo0fVt1d3dXqus/r2f9kBUQLO6b23JuvXnzfpw0/nq5ivn23/yb+i9fWihfrvkmUqX6GiJKnSQ1X0++6d+mbxlxox+vpDgi1atVWLVm114fw5uXt4yGKx6Ksv5qtEiVIZrvXD0m9UqXIVVQl4+N7cHICbcniiWbt2bU2ePFnNmzfXpk2bNGfOHEnXF3L39fW97fFWq9U2VycdDwM5n8TERA3o20tubm6aMWtOhu8JSTp69Ij69AzR00930KBXhzggSsD5GIah999+U5s2rNPsT+bJv4T9snXJV65IUoZ5bC4uLrYixI0KFS4iSVq+9Fu5uVlVp16gXfulS0lav3aV+g8cnIN3Aafg7KVHkzg80Zw+fbq6du2qpUuXavTo0apY8fpvtF9//bWeeOIJB0eHe+FSUpKio6NtX/998qQOHjggb29vFff3V3xcnE6fPq2zZ89Ikk6ciJIkFSlSREWKFlViYqL69+mpK1cua8pb7ygpMVFJiYmSpIKFCsnV1VVHjhxWn54heqJ+A70U0kPnzp6VJLm4uqpQoUL3+I4B5/HuW29o7Y8r9fa0D5QvXz6dP3f9Z8/Ts4Cs7u4qU7acSpYqrbffnKhBQ4bJy9tHmzeu145tEXpnxoe283z91ReqVr2mPPLl046tWzRrxnsaMGiIChTwsrveujWrdC01VUFt2t3T+wSQuft2eaMrV67I1dU10+HP2x5LRfOBsmP7NvXu0S3D/qfbP6M3prylZd99q3FjRmVo7//yQA0IHXTT4yVp5Zp1KlGipObM/kBzP5yVod3fv4R+XLv+7m8C9wzLGz1Ynngs8+Hr0RMmq83Tz0iS/or+U3Nmvq/fI3fr8qVLKlmqlF54qYfdskSTxo7Sll826fKlSypTtlyG9nR9u3eVf4kSmvDmVHNuCKZy5PJG247Fm3buuhW8b98pl7pvE827QaIJ5F4kmkDuRaKZ+zh86Dw1NVXTpk3T4sWLFR0dnWHtzAsXLjgoMgAA4CycfLlL0zh8eaOJEyfq/fffV+fOnRUfH6+wsDB17NhRLi4umjBhgqPDAwAAToDljczh8KHzChUqaObMmWrTpo0KFCigyMhI276tW7dq4cKF2T4nQ+dA7sXQOZB7OXLofMdx84bO65R33qFzh1c0Y2JiVK1aNUmSp6en4uOv/0W3bdtWK1ascGRoAADAWVDSNIXDE82SJUvq9OnTkq5XN9esWSNJ2rFjR6ZrIQIAAODB4PBE85lnntG6deskSYMGDdLYsWNVqVIldevWTT179nRwdAAAwBlYTPzPmTl8jua/RUREKCIiQpUqVVK7dne24C5zNIHcizmaQO7lyDmav0UlmHbu2uW8bt8pl7rvEs2cQKIJ5F4kmkDu5chEc+cJ8xLNWmWdN9F0yN/o999/n+W+Tz+d8c0PAAAAuP85JNHs0KFDlvpZLBalpqaaGwwAAHB6zj2T0jwOSTTT0tIccVkAAIDMkWmawuFPnQMAACB3cliiuX79egUEBCghIePk2/j4eD388MPavHmzAyIDAADOhuWNzOGwRHP69Onq06ePvLwyPonl7e2tfv36adq0aQ6IDAAAADnBYYnm77//rpYtW960vUWLFtq5c+c9jAgAADgri8W8zZk5LNGMjY1V3rx5b9qeJ08enT179h5GBAAAgJzksESzRIkS2rdv303b9+zZo+LFi9/DiAAAgLOymLg5M4clmq1bt9bYsWN15cqVDG2XL1/W+PHj1bZtWwdEBgAAgJzgsFdQxsbG6rHHHpOrq6sGDhyoypUrS5IOHjyo2bNnKzU1Vbt27ZKvr2+2z80rKIHci1dQArmXI19B+ftf/5h27uqlCph27vudQ991/ueff2rAgAFavXq10sOwWCwKCgrS7NmzVa5cuTs6L4kmkHuRaAK5lyMTzT1/JZp27kdLeZp27vudQxPNdBcvXtTRo0dlGIYqVaqkggUL3tX5SDSB3ItEE8i9SDRzH8f9jd6gYMGCqlOnjqPDAAAATsrZlyEyC6+gBAAAgCnui4omAACAI1HQNAcVTQAAAJiCiiYAAAAlTVNQ0QQAAIApqGgCAACnZ6GkaQoqmgAAADAFFU0AAOD0WEfTHCSaAADA6ZFnmoOhcwAAAJiCiiYAAAAlTVNQ0QQAAIApqGgCAACnx/JG5qCiCQAAAFNQ0QQAAE6P5Y3MQUUTAAAApqCiCQAAnB4FTXOQaAIAAJBpmoKhcwAAAJiCiiYAAHB6LG9kDiqaAAAAMAUVTQAA4PRY3sgcVDQBAABgCiqaAADA6VHQNAcVTQAAAJiCiiYAAAAlTVNQ0QQAAE7PYuJ/2TFhwgRZLBa7rUqVKrb2K1euKDQ0VIULF5anp6c6deqk2NhYu3NER0erTZs2ypcvn4oVK6bhw4fr2rVrdn02btyoxx57TFarVRUrVtS8efPu+LO7FRJNAACA+8jDDz+s06dP27ZffvnF1jZkyBD98MMPWrJkiTZt2qRTp06pY8eOtvbU1FS1adNGKSkp2rJli+bPn6958+Zp3Lhxtj5RUVFq06aNmjZtqsjISA0ePFi9e/fW6tWrc/xeLIZhGDl+Vge7cu32fQA8mJKS+QEHcqvC+R03oy/q3BXTzl2uiHuW+06YMEFLly5VZGRkhrb4+HgVLVpUCxcu1LPPPitJOnjwoKpWraqIiAjVq1dPP/74o9q2batTp07J19dXkjR37lyNHDlSZ8+elZubm0aOHKkVK1Zo3759tnMHBwcrLi5Oq1aturub/RcqmgAAACZKTk5WQkKC3ZacnHzT/keOHJG/v7/Kly+vrl27Kjo6WpK0c+dOXb16Vc2bN7f1rVKlikqXLq2IiAhJUkREhKpVq2ZLMiUpKChICQkJ2r9/v63PjedI75N+jpxEogkAAJyexcQtPDxc3t7edlt4eHimcdStW1fz5s3TqlWrNGfOHEVFRalhw4b6559/FBMTIzc3N/n4+Ngd4+vrq5iYGElSTEyMXZKZ3p7edqs+CQkJunz5crY+t9vhqXMAAAATjRo1SmFhYXb7rFZrpn1btWpl+/Ojjz6qunXrqkyZMlq8eLE8PDxMjdMMVDQBAABMLGlarVZ5eXnZbTdLNP/Nx8dHDz30kI4ePSo/Pz+lpKQoLi7Ork9sbKz8/PwkSX5+fhmeQk//+nZ9vLy8cjyZJdEEAAC4TyUmJurYsWMqXry4atWqpbx582rdunW29kOHDik6OlqBgYGSpMDAQO3du1dnzpyx9Vm7dq28vLwUEBBg63PjOdL7pJ8jJ/HUOYAHCk+dA7mXI586//P8zR/OuVtlCmeteilJw4YNU7t27VSmTBmdOnVK48ePV2RkpP744w8VLVpUAwYM0MqVKzVv3jx5eXlp0KBBkqQtW7ZIur68UY0aNeTv76+pU6cqJiZGL730knr37q0pU6ZIur680SOPPKLQ0FD17NlT69ev1yuvvKIVK1YoKCgoR++dOZoAAMDpWe6TNwOdPHlSL7zwgs6fP6+iRYuqQYMG2rp1q4oWLSpJmjZtmlxcXNSpUyclJycrKChIH374oe14V1dXLV++XAMGDFBgYKDy58+vkJAQTZo0ydanXLlyWrFihYYMGaIZM2aoZMmS+vTTT3M8yZSoaAJ4wFDRBHIvR1Y0oy+YV9EsXSjrFc3choomAABwevdJQTPX4WEgAAAAmIKKJgAAcHr3yxzN3IaKJgAAAExBRRMAAIBZmqagogkAAABTUNEEAABOjzma5iDRBAAATo880xwMnQMAAMAUVDQBAIDTY+jcHFQ0AQAAYAoqmgAAwOlZmKVpCiqaAAAAMAUVTQAAAAqapqCiCQAAAFNQ0QQAAE6PgqY5SDQBAIDTY3kjczB0DgAAAFNQ0QQAAE6P5Y3MQUUTAAAApqCiCQAAQEHTFFQ0AQAAYAoqmgAAwOlR0DQHFU0AAACYgoomAABweqyjaQ4STQAA4PRY3sgcDJ0DAADAFFQ0AQCA02Po3BxUNAEAAGAKEk0AAACYgkQTAAAApmCOJgAAcHrM0TQHFU0AAACYgoomAABweqyjaQ4STQAA4PQYOjcHQ+cAAAAwBRVNAADg9ChomoOKJgAAAExBRRMAAICSpimoaAIAAMAUVDQBAIDTY3kjc1DRBAAAgCmoaAIAAKfHOprmoKIJAAAAU1DRBAAATo+CpjlINAEAAMg0TcHQOQAAAExBRRMAADg9ljcyBxVNAAAAmIKKJgAAcHosb2QOKpoAAAAwhcUwDMPRQQB3Kjk5WeHh4Ro1apSsVqujwwGQg/j5Bh58JJp4oCUkJMjb21vx8fHy8vJydDgAchA/38CDj6FzAAAAmIJEEwAAAKYg0QQAAIApSDTxQLNarRo/fjwPCgC5ED/fwIOPh4EAAABgCiqaAAAAMAWJJgAAAExBogkAAABTkGjivmGxWLR06VJHhwHABPx8A86JRBP3RExMjAYNGqTy5cvLarWqVKlSateundatW+fo0CRJhmFo3LhxKl68uDw8PNS8eXMdOXLE0WEBD4T7/ef722+/VYsWLVS4cGFZLBZFRkY6OiTAaZBownQnTpxQrVq1tH79er3zzjvau3evVq1apaZNmyo0NNTR4UmSpk6dqpkzZ2ru3Lnatm2b8ufPr6CgIF25csXRoQH3tQfh5zspKUkNGjTQ22+/7ehQAOdjACZr1aqVUaJECSMxMTFD28WLF21/lmR89913tq9HjBhhVKpUyfDw8DDKlStnjBkzxkhJSbG1R0ZGGk2aNDE8PT2NAgUKGI899pixY8cOwzAM48SJE0bbtm0NHx8fI1++fEZAQICxYsWKTONLS0sz/Pz8jHfeece2Ly4uzrBarcaXX355l3cP5G73+8/3jaKiogxJxu7du+/4fgFkTx4H57nI5S5cuKBVq1bpzTffVP78+TO0+/j43PTYAgUKaN68efL399fevXvVp08fFShQQCNGjJAkde3aVTVr1tScOXPk6uqqyMhI5c2bV5IUGhqqlJQUbd68Wfnz59cff/whT0/PTK8TFRWlmJgYNW/e3LbP29tbdevWVUREhIKDg+/iEwByrwfh5xuAY5FowlRHjx6VYRiqUqVKto8dM2aM7c9ly5bVsGHD9NVXX9n+IYqOjtbw4cNt565UqZKtf3R0tDp16qRq1apJksqXL3/T68TExEiSfH197fb7+vra2gBk9CD8fANwLOZowlTGXbx4atGiRapfv778/Pzk6empMWPGKDo62tYeFham3r17q3nz5nrrrbd07NgxW9srr7yiyZMnq379+ho/frz27NlzV/cBICN+vgHcDokmTFWpUiVZLBYdPHgwW8dFRESoa9euat26tZYvX67du3dr9OjRSklJsfWZMGGC9u/frzZt2mj9+vUKCAjQd999J0nq3bu3jh8/rpdeekl79+5V7dq19cEHH2R6LT8/P0lSbGys3f7Y2FhbG4CMHoSfbwAO5tgponAGLVu2zPbDAu+++65Rvnx5u769evUyvL29b3qd4OBgo127dpm2vfbaa0a1atUybUt/GOjdd9+17YuPj+dhICAL7vef7xvxMBBw71HRhOlmz56t1NRUPf744/rmm2905MgRHThwQDNnzlRgYGCmx1SqVEnR0dH66quvdOzYMc2cOdNWzZCky5cva+DAgdq4caP+/PNP/frrr9qxY4eqVq0qSRo8eLBWr16tqKgo7dq1Sxs2bLC1/ZvFYtHgwYM1efJkff/999q7d6+6desmf39/dejQIcc/DyA3ud9/vqXrDy1FRkbqjz/+kCQdOnRIkZGRzMEG7gVHZ7pwDqdOnTJCQ0ONMmXKGG5ubkaJEiWMp59+2tiwYYOtj/61/Mnw4cONwoULG56enkbnzp2NadOm2SoeycnJRnBwsFGqVCnDzc3N8Pf3NwYOHGhcvnzZMAzDGDhwoFGhQgXDarUaRYsWNV566SXj3LlzN40vLS3NGDt2rOHr62tYrVajWbNmxqFDh8z4KIBc537/+f78888NSRm28ePHm/BpALiRxTDuYjY3AAAAcBMMnQMAAMAUJJoAAAAwBYkmAAAATEGiCQAAAFOQaAIAAMAUJJoAAAAwBYkmAAAATEGiCQAAAFOQaALIMd27d7d7bWeTJk00ePDgex7Hxo0bZbFYFBcXZ9o1/n2vd+JexAkAjkSiCeRy3bt3l8VikcVikZubmypWrKhJkybp2rVrpl/722+/1RtvvJGlvvc66SpbtqymT59+T64FAM4qj6MDAGC+li1b6vPPP1dycrJWrlyp0NBQ5c2bV6NGjcrQNyUlRW5ubjly3UKFCuXIeQAADyYqmoATsFqt8vPzU5kyZTRgwAA1b95c33//vaT/DQG/+eab8vf3V+XKlSVJf/31l55//nn5+PioUKFCat++vU6cOGE7Z2pqqsLCwuTj46PChQtrxIgRMgzD7rr/HjpPTk7WyJEjVapUKVmtVlWsWFH/+c9/dOLECTVt2lSSVLBgQVksFnXv3l2SlJaWpvDwcJUrV04eHh6qXr26vv76a7vrrFy5Ug899JA8PDzUtGlTuzjvRGpqqnr16mW7ZuXKlTVjxoxM+06cOFFFixaVl5eX+vfvr5SUFFtbVmIHgNyMiibghDw8PHT+/Hnb1+vWrZOXl5fWrl0rSbp69aqCgoIUGBion3/+WXny5NHkyZPVsmVL7dmzR25ubnrvvfc0b948ffbZZ6pataree+89fffdd3ryySdvet1u3bopIiJCM2fOVPXq1RUVFaVz586pVKlS+uabb9SpUycdOnRIXl5e8vDwkCSFh4frv//9r+bOnatKlSpp8+bNevHFF1W0aFE1btxYf/31lzp27KjQ0FD17dtXv/32m4YOHXpXn09aWppKliypJUuWqHDhwtqyZYv69u2r4sWL6/nnn7f73Nzd3bVx40adOHFCPXr0UOHChfXmm29mKXYAyPUMALlaSEiI0b59e8MwDCMtLc1Yu3atYbVajWHDhtnafX19jeTkZNsxCxYsMCpXrmykpaXZ9iUnJxseHh7G6tWrDcMwjOLFixtTp061tV+9etUoWbKk7VqGYRiNGzc2Xn31VcMwDOPQoUOGJGPt2rWZxrlhwwZDknHx4kXbvitXrhj58uUztmzZYte3V69exgsvvGAYhmGMGjXKCAgIsGsfOXJkhnP9W5kyZYxp06bdtP3fQkNDjU6dOtm+DgkJMQoVKmQkJSXZ9s2ZM8fw9PQ0UlNTsxR7ZvcMALkJFU3ACSxfvlyenp66evWq0tLS1KVLF02YMMHWXq1aNbt5mb///ruOHj2qAgUK2J3nypUrOnbsmOLj43X69GnVrVvX1pYnTx7Vrl07w/B5usjISLm6umarknf06FFdunRJTz31lN3+lJQU1axZU5J04MABuzgkKTAwMMvXuJnZs2frs88+U3R0tC5fvqyUlBTVqFHDrk/16tWVL18+u+smJibqr7/+UmJi4m1jB4DcjkQTcAJNmzbVnDlz5ObmJn9/f+XJY/+jnz9/fruvExMTVatWLX3xxRcZzlW0aNE7iiF9KDw7EhMTJUkrVqxQiRIl7NqsVusdxZEVX331lYYNG6b33ntPgYGBKlCggN555x1t27Yty+dwVOwAcD8h0QScQP78+VWxYsUs93/ssce0aNEiFStWTF5eXpn2KV68uLZt26ZGjRpJkq5du6adO3fqsccey7R/tWrVlJaWpk2bNql58+YZ2tMrqqmpqbZ9AQEBslqtio6OvmkltGrVqrYHm9Jt3br19jd5C7/++queeOIJvfzyy7Z9x44dy9Dv999/1+XLl21J9NatW+Xp6alSpUqpUKFCt40dAHI7njoHkEHXrl1VpEgRtW/fXj///LOioqK0ceNGvfLKKzp58qQk6dVXX9Vbb72lpUuX6uDBg3r55ZdvuQZm2bJlFRISop49e2rp0qW2cy5evFiSVKZMGVksFi1fvlxnz55VYmKiChQooGHDhmnIkCGaP3++jh07pl27dumDDz7Q/PnzJUn9+/fXkSNHNHz4cB06dEgLFy7UvHnzsnSff//9tyIjI+22ixcvqlKlSvrtt9+0evVqHT58WGPHjtWOHTsyHJ+SkqJevXrpjz/+0MqVKzV+/HgNHDhQLi4uWYodAHI9R08SBWCuGx8Gyk776dOnjW7duhlFihQxrFarUb58eaNPnz5GfHy8YRjXH/559dVXDS8vL8PHx8cICwszunXrdtOHgQzDMC5fvmwMGTLEKF68uOHm5mZUrFjR+Oyzz2ztkyZNMvz8/AyLxWKEhIQYhnH9Aabp06cblStXNvLmzWsULVrUCAoKMjZt2mQ77ocffjAqVqxoWK1Wo2HDhsZnn32WpYeBJGXYFixYYFy5csXo3r274e3tbfj4+BgDBgwwXnvtNaN69eoZPrdx48YZhQsXNjw9PY0+ffoYV65csfW5Xew8DAQgt7MYxk1m7gMAAAB3gaFzAAAAmIJEEwAAAKYg0QQAAIApSDQBAABgChJNAAAAmIJEEwAAAKYg0QQAAIApSDQBAABgChJNAAAAmIJEEwAAAKYg0QQAAIAp/h/XqNFT+0ZRFgAAAABJRU5ErkJggg==\n"},"metadata":{}},{"name":"stdout","text":"\n===== Fold 2/5 =====\n[0]\tvalidation_0-logloss:0.55715\n[100]\tvalidation_0-logloss:0.51724\n[200]\tvalidation_0-logloss:0.49301\n[300]\tvalidation_0-logloss:0.47561\n[400]\tvalidation_0-logloss:0.46086\n[500]\tvalidation_0-logloss:0.44881\n[600]\tvalidation_0-logloss:0.43826\n[700]\tvalidation_0-logloss:0.42936\n[800]\tvalidation_0-logloss:0.42132\n[900]\tvalidation_0-logloss:0.41419\n[1000]\tvalidation_0-logloss:0.40780\n[1100]\tvalidation_0-logloss:0.40221\n[1200]\tvalidation_0-logloss:0.39690\n[1300]\tvalidation_0-logloss:0.39241\n[1400]\tvalidation_0-logloss:0.38772\n[1500]\tvalidation_0-logloss:0.38396\n[1600]\tvalidation_0-logloss:0.37995\n[1700]\tvalidation_0-logloss:0.37663\n[1800]\tvalidation_0-logloss:0.37348\n[1900]\tvalidation_0-logloss:0.37018\n[2000]\tvalidation_0-logloss:0.36762\n[2100]\tvalidation_0-logloss:0.36488\n[2200]\tvalidation_0-logloss:0.36237\n[2300]\tvalidation_0-logloss:0.36019\n[2400]\tvalidation_0-logloss:0.35806\n[2500]\tvalidation_0-logloss:0.35585\n[2600]\tvalidation_0-logloss:0.35396\n[2700]\tvalidation_0-logloss:0.35217\n[2800]\tvalidation_0-logloss:0.35052\n[2900]\tvalidation_0-logloss:0.34900\n[3000]\tvalidation_0-logloss:0.34754\n[3100]\tvalidation_0-logloss:0.34605\n[3200]\tvalidation_0-logloss:0.34468\n[3300]\tvalidation_0-logloss:0.34293\n[3400]\tvalidation_0-logloss:0.34160\n[3500]\tvalidation_0-logloss:0.34059\n[3600]\tvalidation_0-logloss:0.33965\n[3700]\tvalidation_0-logloss:0.33848\n[3800]\tvalidation_0-logloss:0.33738\n[3900]\tvalidation_0-logloss:0.33637\n[4000]\tvalidation_0-logloss:0.33549\n[4100]\tvalidation_0-logloss:0.33451\n[4200]\tvalidation_0-logloss:0.33351\n[4300]\tvalidation_0-logloss:0.33278\n[4400]\tvalidation_0-logloss:0.33212\n[4500]\tvalidation_0-logloss:0.33128\n[4600]\tvalidation_0-logloss:0.33030\n[4646]\tvalidation_0-logloss:0.33027\n\n=== (Train Set)_2 ===\nAccuracy: 0.8808\nAUC: 0.9107\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.97      0.90      0.93    143921\n           1       0.44      0.74      0.56     16079\n\n    accuracy                           0.88    160000\n   macro avg       0.71      0.82      0.74    160000\nweighted avg       0.92      0.88      0.89    160000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n=== (Validation Set)_2 ===\nAccuracy: 0.8755\nAUC: 0.8988\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.97      0.89      0.93     35981\n           1       0.43      0.72      0.54      4019\n\n    accuracy                           0.88     40000\n   macro avg       0.70      0.80      0.73     40000\nweighted avg       0.91      0.88      0.89     40000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n===== Fold 3/5 =====\n[0]\tvalidation_0-logloss:0.55724\n[100]\tvalidation_0-logloss:0.51722\n[200]\tvalidation_0-logloss:0.49236\n[300]\tvalidation_0-logloss:0.47477\n[400]\tvalidation_0-logloss:0.46005\n[500]\tvalidation_0-logloss:0.44825\n[600]\tvalidation_0-logloss:0.43735\n[700]\tvalidation_0-logloss:0.42840\n[800]\tvalidation_0-logloss:0.42026\n[900]\tvalidation_0-logloss:0.41317\n[1000]\tvalidation_0-logloss:0.40684\n[1100]\tvalidation_0-logloss:0.40115\n[1200]\tvalidation_0-logloss:0.39559\n[1300]\tvalidation_0-logloss:0.39110\n[1400]\tvalidation_0-logloss:0.38658\n[1500]\tvalidation_0-logloss:0.38258\n[1600]\tvalidation_0-logloss:0.37892\n[1700]\tvalidation_0-logloss:0.37527\n[1800]\tvalidation_0-logloss:0.37222\n[1900]\tvalidation_0-logloss:0.36914\n[2000]\tvalidation_0-logloss:0.36617\n[2100]\tvalidation_0-logloss:0.36369\n[2200]\tvalidation_0-logloss:0.36139\n[2300]\tvalidation_0-logloss:0.35898\n[2400]\tvalidation_0-logloss:0.35695\n[2500]\tvalidation_0-logloss:0.35499\n[2600]\tvalidation_0-logloss:0.35283\n[2700]\tvalidation_0-logloss:0.35120\n[2800]\tvalidation_0-logloss:0.34946\n[2900]\tvalidation_0-logloss:0.34775\n[3000]\tvalidation_0-logloss:0.34610\n[3100]\tvalidation_0-logloss:0.34472\n[3200]\tvalidation_0-logloss:0.34297\n[3300]\tvalidation_0-logloss:0.34179\n[3400]\tvalidation_0-logloss:0.34069\n[3500]\tvalidation_0-logloss:0.33939\n[3600]\tvalidation_0-logloss:0.33816\n[3700]\tvalidation_0-logloss:0.33721\n[3800]\tvalidation_0-logloss:0.33595\n[3900]\tvalidation_0-logloss:0.33503\n[4000]\tvalidation_0-logloss:0.33411\n[4100]\tvalidation_0-logloss:0.33315\n[4200]\tvalidation_0-logloss:0.33248\n[4300]\tvalidation_0-logloss:0.33142\n[4400]\tvalidation_0-logloss:0.33078\n[4500]\tvalidation_0-logloss:0.32982\n[4600]\tvalidation_0-logloss:0.32923\n[4700]\tvalidation_0-logloss:0.32862\n[4800]\tvalidation_0-logloss:0.32799\n[4900]\tvalidation_0-logloss:0.32712\n[4958]\tvalidation_0-logloss:0.32706\n\n=== (Train Set)_3 ===\nAccuracy: 0.8813\nAUC: 0.9128\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.97      0.90      0.93    143922\n           1       0.45      0.75      0.56     16078\n\n    accuracy                           0.88    160000\n   macro avg       0.71      0.82      0.75    160000\nweighted avg       0.92      0.88      0.89    160000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n=== (Validation Set)_3 ===\nAccuracy: 0.8758\nAUC: 0.8926\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.96      0.89      0.93     35980\n           1       0.43      0.70      0.53      4020\n\n    accuracy                           0.88     40000\n   macro avg       0.70      0.80      0.73     40000\nweighted avg       0.91      0.88      0.89     40000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n===== Fold 4/5 =====\n[0]\tvalidation_0-logloss:0.55732\n[100]\tvalidation_0-logloss:0.51812\n[200]\tvalidation_0-logloss:0.49406\n","output_type":"stream"}],"execution_count":null},{"cell_type":"markdown","source":"# 保存树模型","metadata":{}},{"cell_type":"code","source":"i=0\nfor xgboost_model in xgboost_models:\n    i+=1\n    booster = xgboost_model.get_booster()\n    \n    # 导出第0棵树\n    dot = to_graphviz(booster, num_trees=0, rankdir='LR')\n    dot.render(f'xgb_tree_{i}_0', format='pdf')  # 会生成 xgb_tree_0.pdf","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 特征重要性","metadata":{}},{"cell_type":"code","source":"# ====== 特征重要性（如需可关闭/挪到外部）======\ni=0\nfor xgboost_model in xgboost_models:\n    i+=1\n    plot_feature_importance(xgboost_model, top_n=20, save_path=f\"/kaggle/working/xgboost_evaluation/xgboost_feature_importance_{i}.png\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-11T14:42:22.353943Z","iopub.status.idle":"2025-09-11T14:42:22.354222Z","shell.execute_reply.started":"2025-09-11T14:42:22.354062Z","shell.execute_reply":"2025-09-11T14:42:22.354076Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 非集成简单平均预测test","metadata":{}},{"cell_type":"code","source":"test_pred = np.zeros(len(test))\nfor model in xgboost_models:\n    test_pred += model.predict_proba(test)[:, 1]","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 输出预测结果","metadata":{}},{"cell_type":"code","source":"sub = pd.read_csv('/kaggle/input/santander-customer-transaction-prediction/sample_submission.csv')\nsub['target'] = 0.0\n# 对于 real_samples_indexes 中的索引，填充预测值\nsub.loc[real_samples_indexes, 'target'] = preds_test_cum\nsub.to_csv('/kaggle/working/submission.csv',index=False)\nsub.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T09:38:26.365288Z","iopub.execute_input":"2025-09-16T09:38:26.366070Z","iopub.status.idle":"2025-09-16T09:38:26.906013Z","shell.execute_reply.started":"2025-09-16T09:38:26.366033Z","shell.execute_reply":"2025-09-16T09:38:26.905134Z"}},"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"  ID_code     target\n0  test_0   0.000000\n1  test_1   0.000000\n2  test_2   0.000000\n3  test_3  63.482169\n4  test_4   0.000000","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID_code</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>test_0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>test_1</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>test_2</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>test_3</td>\n      <td>63.482169</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>test_4</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":31},{"cell_type":"markdown","source":"# 划分训练和验证便于后续调参","metadata":{}},{"cell_type":"code","source":"x_train, x_val, y_train, y_val = train_test_split(train_x, train_y, test_size=0.2, random_state=42)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 随机网格搜索调优","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport xgboost as xgb\nfrom sklearn.model_selection import train_test_split, RandomizedSearchCV, PredefinedSplit\nfrom sklearn.metrics import roc_auc_score\n\n# --- 小工具：根据是否用GPU返回合适的参数（避免使用 gpu_hist） ---\ndef _device_params(use_gpu: bool):\n    \"\"\"\n    XGBoost>=2.0 推荐：\n      - CPU: tree_method='hist'\n      - GPU: tree_method='hist', device='cuda'\n    \"\"\"\n    if use_gpu:\n        # 若环境无GPU，XGBoost会自行回退到CPU；这里不再使用 gpu_hist\n        return {\"tree_method\": \"hist\", \"device\": \"cuda\"}\n    else:\n        return {\"tree_method\": \"hist\"}  # 纯CPU\n\ndef stratified_subsample(X, y, frac=0.1, random_state=42):\n    \"\"\"分层抽样：用于快速搜参（修正为只抽一次，避免不一致）\"\"\"\n    X_sub, _, y_sub, _ = train_test_split(\n        X, y, test_size=1 - frac, stratify=y, random_state=random_state\n    )\n    return X_sub, y_sub\n\ndef quick_tune_xgb(X_train, y_train, X_val, y_val, n_iter=25, random_state=42, use_gpu=False, frac=0.15):\n    \"\"\"在小样本上快速搜参，然后返回最优参数\"\"\"\n    X_sub, y_sub = stratified_subsample(X_train, y_train, frac=frac, random_state=random_state)\n\n    # 兼容 pandas\n    X_sub_np = X_sub.values if hasattr(X_sub, \"values\") else np.asarray(X_sub)\n    X_val_np = X_val.values if hasattr(X_val, \"values\") else np.asarray(X_val)\n    y_sub_np = np.asarray(y_sub)\n    y_val_np = np.asarray(y_val)\n\n    # 固定验证折\n    X_all = np.vstack([X_sub_np, X_val_np])\n    y_all = np.concatenate([y_sub_np, y_val_np])\n    test_fold = np.concatenate([\n        -1 * np.ones(len(X_sub_np), dtype=int),\n         0 * np.ones(len(X_val_np), dtype=int)\n    ])\n    ps = PredefinedSplit(test_fold)\n\n    # 类别不平衡权重（基于子样本）\n    pos = np.sum(y_sub_np == 1)\n    neg = np.sum(y_sub_np == 0)\n    spw = (neg / pos) if pos > 0 else 1.0\n\n    base = xgb.XGBClassifier(\n        objective='binary:logistic',\n        eval_metric='auc',           # 与 scoring 对齐\n        n_estimators=3000,\n        early_stopping_rounds=50,\n        random_state=random_state,\n        n_jobs=-1,\n        **_device_params(use_gpu)\n    )\n\n    param_dist = {\n        \"learning_rate\":    [0.03, 0.05, 0.07, 0.1, 0.15],\n        \"max_depth\":        [3, 4, 5, 6, 8],\n        \"min_child_weight\": [1, 2, 3, 5, 8],\n        \"subsample\":        [0.7, 0.8, 0.9, 1.0],\n        \"colsample_bytree\": [0.7, 0.8, 0.9, 1.0],\n        \"reg_alpha\":        [0.0, 0.1, 0.5, 1.0],\n        \"reg_lambda\":       [0.5, 1.0, 2.0, 5.0],\n        \"scale_pos_weight\": [spw * 0.5, spw, spw * 1.5, spw * 2.0]\n    }\n\n    search = RandomizedSearchCV(\n        estimator=base,\n        param_distributions=param_dist,\n        n_iter=n_iter,\n        scoring=\"roc_auc\",\n        cv=ps,\n        verbose=1,\n        n_jobs=-1,\n        random_state=random_state,\n        refit=True\n    )\n\n    search.fit(X_all, y_all, eval_set=[(X_val_np, y_val_np)], verbose=False)\n    print(\"快速搜参最优参数：\", search.best_params_)\n    print(\"快速搜参最佳AUC：\", search.best_score_)\n    return search.best_params_\n\ndef train_full_xgb(X_train, y_train, X_val, y_val, best_params, random_state=42, use_gpu=False):\n    \"\"\"用全量数据+早停做精修训练\"\"\"\n    X_train_np = X_train.values if hasattr(X_train, \"values\") else np.asarray(X_train)\n    X_val_np   = X_val.values   if hasattr(X_val, \"values\")   else np.asarray(X_val)\n    y_train_np = np.asarray(y_train)\n    y_val_np   = np.asarray(y_val)\n\n    model = xgb.XGBClassifier(\n        objective='binary:logistic',\n        eval_metric='auc',\n        n_estimators=5000,\n        early_stopping_rounds=100,\n        random_state=random_state,\n        n_jobs=-1,\n        **_device_params(use_gpu),\n        **best_params\n    )\n    model.fit(X_train_np, y_train_np, eval_set=[(X_val_np, y_val_np)], verbose=100)\n\n    val_auc = roc_auc_score(y_val_np, model.predict_proba(X_val_np)[:, 1])\n    print(\"全量精修 AUC:\", val_auc)\n    return model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-11T14:42:22.357621Z","iopub.status.idle":"2025-09-11T14:42:22.357923Z","shell.execute_reply.started":"2025-09-11T14:42:22.357778Z","shell.execute_reply":"2025-09-11T14:42:22.357791Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 1) 小样本快速搜参\n# best_params = quick_tune_xgb(x_train, y_train, x_val, y_val, n_iter=25, use_gpu=False)\n\n# # 2) 全量数据精修训练（早停）\n# best_model = train_full_xgb(x_train, y_train, x_val, y_val, best_params, use_gpu=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-11T14:42:22.358957Z","iopub.status.idle":"2025-09-11T14:42:22.359164Z","shell.execute_reply.started":"2025-09-11T14:42:22.359072Z","shell.execute_reply":"2025-09-11T14:42:22.359081Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# optuna搜索调优","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport optuna\nimport xgboost as xgb\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\nfrom catboost import Pool\n\n# --- 小工具：根据是否用GPU返回合适的参数（避免使用 gpu_hist） ---\ndef _device_params(use_gpu: bool):\n    if use_gpu:\n        return {\"tree_method\": \"hist\", \"device\": \"cuda\"}  # GPU版本\n    else:\n        return {\"tree_method\": \"hist\"}  # CPU版本\n\ndef stratified_subsample(X, y, frac=0.1, random_state=42):\n    \"\"\"分层抽样：用于快速搜参（修正为只抽一次，避免不一致）\"\"\"\n    X_sub, _, y_sub, _ = train_test_split(\n        X, y, test_size=1 - frac, stratify=y, random_state=random_state\n    )\n    return X_sub, y_sub\n\ndef objective(trial, X_train, y_train, X_val, y_val, frac=0.15, use_gpu=False):\n    \"\"\"Optuna优化目标函数\"\"\"\n    # 子样本（搜参更快）\n    X_sub, y_sub = stratified_subsample(X_train, y_train, frac=frac, random_state=42)\n\n    # 定义超参数空间\n    param = {\n        \"learning_rate\": trial.suggest_float('learning_rate', 0.01, 0.1),\n        \"max_depth\": trial.suggest_int('max_depth', 2, 4),\n        \"min_child_weight\": trial.suggest_int('min_child_weight', 1, 10),\n        \"subsample\": trial.suggest_float('subsample', 0.7, 1.0),  # 替换为 suggest_float\n        \"colsample_bytree\": trial.suggest_float('colsample_bytree', 0.7, 1.0),  # 替换为 suggest_float\n        \"reg_alpha\": trial.suggest_float('reg_alpha', 1e-5, 1.0, log=True),  # 使用 log=True 替代 suggest_loguniform\n        \"reg_lambda\": trial.suggest_float('reg_lambda', 1e-5, 5.0, log=True),  # 使用 log=True 替代 suggest_loguniform\n        \"gamma\": trial.suggest_float('gamma', 0.0, 5.0),  # 添加 gamma 来防止过拟合\n        \"max_delta_step\": trial.suggest_int('max_delta_step', 0, 10),  # 增加 max_delta_step\n        \"scale_pos_weight\": trial.suggest_float('scale_pos_weight', 0.5, 2.0),  # 根据类别不平衡调整\n    }\n\n    # 类别不平衡（基于子样本）\n    pos = np.sum(y_sub == 1)\n    neg = np.sum(y_sub == 0)\n    spw = (neg / pos) if pos > 0 else 1.0\n    param['scale_pos_weight'] = spw * param['scale_pos_weight']\n\n    # 创建 XGBoost 模型\n    model = xgb.XGBClassifier(\n        objective='binary:logistic',\n        eval_metric='auc',\n        n_estimators=3000,\n        early_stopping_rounds=50,\n        random_state=42,\n        n_jobs=-1,\n        **_device_params(use_gpu),\n        **param\n    )\n\n    # 转换为 NumPy 数组\n    X_sub_np = X_sub.values if hasattr(X_sub, \"values\") else np.asarray(X_sub)\n    X_val_np = X_val.values if hasattr(X_val, \"values\") else np.asarray(X_val)\n    y_sub_np = np.asarray(y_sub)\n    y_val_np = np.asarray(y_val)\n\n    # 训练模型并评估 AUC\n    model.fit(X_sub_np, y_sub_np, eval_set=[(X_val_np, y_val_np)], verbose=False)\n    val_auc = roc_auc_score(y_val_np, model.predict_proba(X_val_np)[:, 1])\n\n    return val_auc  # 目标是最大化 AUC\n\n\ndef tune_xgb_with_optuna(X_train, y_train, X_val, y_val, n_trials=25, use_gpu=False):\n    \"\"\"使用 Optuna 优化 XGBoost 超参数\"\"\"\n    study = optuna.create_study(direction='maximize')  # 目标是最大化 AUC\n    study.optimize(lambda trial: objective(trial, X_train, y_train, X_val, y_val, use_gpu=use_gpu), n_trials=n_trials)\n\n    print(\"Best parameters:\", study.best_params)\n    print(\"Best AUC:\", study.best_value)\n    return study.best_params\n\ndef train_full_xgb(X_train, y_train, X_val, y_val, best_params, use_gpu=False):\n    \"\"\"用全量数据+早停做精修训练\"\"\"\n    # 转 numpy\n    X_train_np = X_train.values if hasattr(X_train, \"values\") else np.asarray(X_train)\n    X_val_np = X_val.values if hasattr(X_val, \"values\") else np.asarray(X_val)\n    y_train_np = np.asarray(y_train)\n    y_val_np = np.asarray(y_val)\n\n    # 创建最终模型\n    model = xgb.XGBClassifier(\n        objective='binary:logistic',\n        eval_metric='auc',\n        n_estimators=5000,\n        early_stopping_rounds=100,\n        random_state=42,\n        n_jobs=-1,\n        **_device_params(use_gpu),\n        **best_params\n    )\n\n    # 训练模型\n    model.fit(X_train_np, y_train_np, eval_set=[(X_val_np, y_val_np)], verbose=100)\n\n    # 验证集 AUC\n    val_auc = roc_auc_score(y_val_np, model.predict_proba(X_val_np)[:, 1])\n    print(\"Final model AUC:\", val_auc)\n    return model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-11T14:42:50.679104Z","iopub.execute_input":"2025-09-11T14:42:50.679371Z","iopub.status.idle":"2025-09-11T14:42:51.224315Z","shell.execute_reply.started":"2025-09-11T14:42:50.679349Z","shell.execute_reply":"2025-09-11T14:42:51.223559Z"}},"outputs":[],"execution_count":12},{"cell_type":"code","source":"best_params = tune_xgb_with_optuna(x_train, y_train, x_val, y_val, n_trials=50, use_gpu=True)\nmodel = train_full_xgb(x_train, y_train, x_val, y_val, best_params, use_gpu=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-11T14:42:55.038251Z","iopub.execute_input":"2025-09-11T14:42:55.039019Z","iopub.status.idle":"2025-09-11T14:46:26.323270Z","shell.execute_reply.started":"2025-09-11T14:42:55.038995Z","shell.execute_reply":"2025-09-11T14:46:26.322622Z"}},"outputs":[{"name":"stderr","text":"[I 2025-09-11 14:42:55,040] A new study created in memory with name: no-name-52cd5965-0674-4f03-a8e1-ee98ac59a20c\n/usr/local/lib/python3.11/dist-packages/xgboost/core.py:160: UserWarning: [14:43:01] WARNING: /workspace/src/common/error_msg.cc:58: Falling back to prediction using DMatrix due to mismatched devices. This might lead to higher memory usage and slower performance. XGBoost is running on: cuda:0, while the input data is on: cpu.\nPotential solutions:\n- Use a data structure that matches the device ordinal in the booster.\n- Set the device for booster before call to inplace_predict.\n\nThis warning will only be shown once.\n\n  warnings.warn(smsg, UserWarning)\n[I 2025-09-11 14:43:01,731] Trial 0 finished with value: 0.8646919246677126 and parameters: {'learning_rate': 0.03308613782737353, 'max_depth': 3, 'min_child_weight': 2, 'subsample': 0.9631437489363707, 'colsample_bytree': 0.9604056926377817, 'reg_alpha': 5.387771893427272e-05, 'reg_lambda': 0.11661440777793478, 'gamma': 3.136872106082746, 'max_delta_step': 6, 'scale_pos_weight': 1.6590068053145384}. Best is trial 0 with value: 0.8646919246677126.\n[I 2025-09-11 14:43:06,356] Trial 1 finished with value: 0.8693745237715776 and parameters: {'learning_rate': 0.04842506342541444, 'max_depth': 3, 'min_child_weight': 8, 'subsample': 0.8723810028808282, 'colsample_bytree': 0.7894317380981647, 'reg_alpha': 0.1024992837861997, 'reg_lambda': 0.7416646693162093, 'gamma': 2.92764808946795, 'max_delta_step': 0, 'scale_pos_weight': 1.3710927122475132}. Best is trial 1 with value: 0.8693745237715776.\n[I 2025-09-11 14:43:09,205] Trial 2 finished with value: 0.8755752786314217 and parameters: {'learning_rate': 0.09700396760002794, 'max_depth': 2, 'min_child_weight': 8, 'subsample': 0.7904278892618672, 'colsample_bytree': 0.7904030094889968, 'reg_alpha': 0.0005327367655063133, 'reg_lambda': 0.0009869809970857965, 'gamma': 0.9857165719334537, 'max_delta_step': 1, 'scale_pos_weight': 0.5650852184966346}. Best is trial 2 with value: 0.8755752786314217.\n[I 2025-09-11 14:43:12,332] Trial 3 finished with value: 0.8744401859073117 and parameters: {'learning_rate': 0.09748235774816771, 'max_depth': 2, 'min_child_weight': 3, 'subsample': 0.8191325128999332, 'colsample_bytree': 0.8098691767349026, 'reg_alpha': 0.07440299130984737, 'reg_lambda': 5.3182736123298886e-05, 'gamma': 0.8262240974811841, 'max_delta_step': 2, 'scale_pos_weight': 0.9283696492074016}. Best is trial 2 with value: 0.8755752786314217.\n[I 2025-09-11 14:43:16,046] Trial 4 finished with value: 0.8743057112140854 and parameters: {'learning_rate': 0.06981946182160254, 'max_depth': 2, 'min_child_weight': 2, 'subsample': 0.8620866564352767, 'colsample_bytree': 0.8475880308907431, 'reg_alpha': 1.3799272653448409e-05, 'reg_lambda': 7.43645226520844e-05, 'gamma': 2.467385658254311, 'max_delta_step': 9, 'scale_pos_weight': 1.224451926963182}. Best is trial 2 with value: 0.8755752786314217.\n[I 2025-09-11 14:43:23,069] Trial 5 finished with value: 0.8693964246448737 and parameters: {'learning_rate': 0.0268587344680771, 'max_depth': 3, 'min_child_weight': 2, 'subsample': 0.9378649299772813, 'colsample_bytree': 0.7462715086848164, 'reg_alpha': 0.008311245880014757, 'reg_lambda': 0.18795966841068085, 'gamma': 0.14240238772617653, 'max_delta_step': 3, 'scale_pos_weight': 1.3766923121228056}. Best is trial 2 with value: 0.8755752786314217.\n[I 2025-09-11 14:43:26,229] Trial 6 finished with value: 0.8698727066041471 and parameters: {'learning_rate': 0.09423701191847683, 'max_depth': 3, 'min_child_weight': 9, 'subsample': 0.8240851519521539, 'colsample_bytree': 0.7693303950356131, 'reg_alpha': 0.00018399845368793986, 'reg_lambda': 0.05046344910853723, 'gamma': 2.081927003237583, 'max_delta_step': 6, 'scale_pos_weight': 0.8922510664436878}. Best is trial 2 with value: 0.8755752786314217.\n[I 2025-09-11 14:43:31,756] Trial 7 finished with value: 0.8744008574727264 and parameters: {'learning_rate': 0.04421960758208571, 'max_depth': 2, 'min_child_weight': 9, 'subsample': 0.8517121418152189, 'colsample_bytree': 0.864521869327552, 'reg_alpha': 0.0002694563154512186, 'reg_lambda': 0.1044427995316071, 'gamma': 0.5207701989690999, 'max_delta_step': 3, 'scale_pos_weight': 1.4448338833960643}. Best is trial 2 with value: 0.8755752786314217.\n[I 2025-09-11 14:43:34,923] Trial 8 finished with value: 0.8719974346303462 and parameters: {'learning_rate': 0.08132040468463587, 'max_depth': 2, 'min_child_weight': 6, 'subsample': 0.8068066139126049, 'colsample_bytree': 0.7392807760986585, 'reg_alpha': 1.3331356508625206e-05, 'reg_lambda': 2.762600348654688e-05, 'gamma': 0.5313089861641229, 'max_delta_step': 6, 'scale_pos_weight': 1.578534186133546}. Best is trial 2 with value: 0.8755752786314217.\n[I 2025-09-11 14:43:39,369] Trial 9 finished with value: 0.8725954273872655 and parameters: {'learning_rate': 0.03848111996488176, 'max_depth': 2, 'min_child_weight': 9, 'subsample': 0.7658188173173569, 'colsample_bytree': 0.7248338596269036, 'reg_alpha': 0.520194733373757, 'reg_lambda': 0.00012304061523262192, 'gamma': 2.957137926625726, 'max_delta_step': 8, 'scale_pos_weight': 1.8598598701263502}. Best is trial 2 with value: 0.8755752786314217.\n[I 2025-09-11 14:43:42,823] Trial 10 finished with value: 0.8649546229745456 and parameters: {'learning_rate': 0.06485808158038761, 'max_depth': 4, 'min_child_weight': 6, 'subsample': 0.7433511438846335, 'colsample_bytree': 0.9274505027913995, 'reg_alpha': 0.0019683217424596175, 'reg_lambda': 0.0027420081336446693, 'gamma': 4.751070258190545, 'max_delta_step': 0, 'scale_pos_weight': 0.7610748612115119}. Best is trial 2 with value: 0.8755752786314217.\n[I 2025-09-11 14:43:45,758] Trial 11 finished with value: 0.8760221305486345 and parameters: {'learning_rate': 0.09508201516796844, 'max_depth': 2, 'min_child_weight': 5, 'subsample': 0.7094577764673969, 'colsample_bytree': 0.8176667622412366, 'reg_alpha': 0.012514107632142623, 'reg_lambda': 0.0009498184501674723, 'gamma': 1.4853312444069537, 'max_delta_step': 2, 'scale_pos_weight': 0.5411263578816015}. Best is trial 11 with value: 0.8760221305486345.\n[I 2025-09-11 14:43:49,336] Trial 12 finished with value: 0.8668122643612368 and parameters: {'learning_rate': 0.0811807657421756, 'max_depth': 4, 'min_child_weight': 4, 'subsample': 0.7027570115183969, 'colsample_bytree': 0.8529479179502595, 'reg_alpha': 0.0033241498883003835, 'reg_lambda': 0.0007724623739577757, 'gamma': 1.5295179833905554, 'max_delta_step': 2, 'scale_pos_weight': 0.5110942052109113}. Best is trial 11 with value: 0.8760221305486345.\n[I 2025-09-11 14:43:52,457] Trial 13 finished with value: 0.8775719835952364 and parameters: {'learning_rate': 0.0852998098190656, 'max_depth': 2, 'min_child_weight': 7, 'subsample': 0.7044280719053463, 'colsample_bytree': 0.9001136361178146, 'reg_alpha': 0.01810371280696134, 'reg_lambda': 0.0008397082682177874, 'gamma': 1.4318337351673813, 'max_delta_step': 4, 'scale_pos_weight': 0.5045054439876477}. Best is trial 13 with value: 0.8775719835952364.\n[I 2025-09-11 14:43:55,572] Trial 14 finished with value: 0.8746125511848359 and parameters: {'learning_rate': 0.08081949112046898, 'max_depth': 2, 'min_child_weight': 5, 'subsample': 0.7003629828892025, 'colsample_bytree': 0.902763651640436, 'reg_alpha': 0.02064467319716535, 'reg_lambda': 0.015057272321679796, 'gamma': 1.7754170990437301, 'max_delta_step': 4, 'scale_pos_weight': 1.0479326350612848}. Best is trial 13 with value: 0.8775719835952364.\n[I 2025-09-11 14:44:03,533] Trial 15 finished with value: 0.8759049882398461 and parameters: {'learning_rate': 0.01608030114958898, 'max_depth': 3, 'min_child_weight': 7, 'subsample': 0.742170891813966, 'colsample_bytree': 0.9821178028194462, 'reg_alpha': 0.035980722914353544, 'reg_lambda': 0.00032462719868337674, 'gamma': 3.826899914671011, 'max_delta_step': 4, 'scale_pos_weight': 0.6789899487553497}. Best is trial 13 with value: 0.8775719835952364.\n[I 2025-09-11 14:44:07,422] Trial 16 finished with value: 0.8759276845196844 and parameters: {'learning_rate': 0.0611868612255214, 'max_depth': 2, 'min_child_weight': 4, 'subsample': 0.9106345398038007, 'colsample_bytree': 0.8869136696973249, 'reg_alpha': 0.35678122711222915, 'reg_lambda': 0.007616946807576923, 'gamma': 1.353148643044633, 'max_delta_step': 5, 'scale_pos_weight': 0.7274240697699368}. Best is trial 13 with value: 0.8775719835952364.\n[I 2025-09-11 14:44:11,053] Trial 17 finished with value: 0.8624382082139241 and parameters: {'learning_rate': 0.0738961516028046, 'max_depth': 4, 'min_child_weight': 5, 'subsample': 0.7348158014342494, 'colsample_bytree': 0.825506131087558, 'reg_alpha': 0.00193848851583207, 'reg_lambda': 1.1086576098316382e-05, 'gamma': 2.155655314099501, 'max_delta_step': 8, 'scale_pos_weight': 1.0691217843909557}. Best is trial 13 with value: 0.8775719835952364.\n[I 2025-09-11 14:44:14,367] Trial 18 finished with value: 0.8770616419199262 and parameters: {'learning_rate': 0.0865108868136705, 'max_depth': 2, 'min_child_weight': 7, 'subsample': 0.7734719840533789, 'colsample_bytree': 0.9278768504285737, 'reg_alpha': 0.010055642146000078, 'reg_lambda': 0.0021681392587666196, 'gamma': 1.2229514767371235, 'max_delta_step': 4, 'scale_pos_weight': 0.5074476439916308}. Best is trial 13 with value: 0.8775719835952364.\n[I 2025-09-11 14:44:18,566] Trial 19 finished with value: 0.8733860308976283 and parameters: {'learning_rate': 0.05523166189085173, 'max_depth': 3, 'min_child_weight': 10, 'subsample': 0.7789199692881199, 'colsample_bytree': 0.9493737198085356, 'reg_alpha': 0.005368001086264728, 'reg_lambda': 0.004809168911589754, 'gamma': 1.2123614937564846, 'max_delta_step': 5, 'scale_pos_weight': 0.8587280434503447}. Best is trial 13 with value: 0.8775719835952364.\n[I 2025-09-11 14:44:21,742] Trial 20 finished with value: 0.8675793489918335 and parameters: {'learning_rate': 0.08792120314870051, 'max_depth': 3, 'min_child_weight': 7, 'subsample': 0.7639225883166871, 'colsample_bytree': 0.9950218383848518, 'reg_alpha': 0.15960175945395674, 'reg_lambda': 0.00023389779454947152, 'gamma': 0.22358768037956622, 'max_delta_step': 7, 'scale_pos_weight': 1.1351241156080412}. Best is trial 13 with value: 0.8775719835952364.\n[I 2025-09-11 14:44:24,604] Trial 21 finished with value: 0.8769688954776046 and parameters: {'learning_rate': 0.08904854941586379, 'max_depth': 2, 'min_child_weight': 7, 'subsample': 0.7237069980585328, 'colsample_bytree': 0.9162946038692906, 'reg_alpha': 0.018381281568436894, 'reg_lambda': 0.0015604092349425257, 'gamma': 1.783369200421035, 'max_delta_step': 3, 'scale_pos_weight': 0.5020481329308909}. Best is trial 13 with value: 0.8775719835952364.\n[I 2025-09-11 14:44:27,491] Trial 22 finished with value: 0.8770844401749619 and parameters: {'learning_rate': 0.08664666000510227, 'max_depth': 2, 'min_child_weight': 7, 'subsample': 0.7234807185136019, 'colsample_bytree': 0.9159015035965289, 'reg_alpha': 0.03151841739595208, 'reg_lambda': 0.002169257844699246, 'gamma': 1.917923472491169, 'max_delta_step': 4, 'scale_pos_weight': 0.6525419026208651}. Best is trial 13 with value: 0.8775719835952364.\n[I 2025-09-11 14:44:31,252] Trial 23 finished with value: 0.8780184174141386 and parameters: {'learning_rate': 0.0744725314750152, 'max_depth': 2, 'min_child_weight': 8, 'subsample': 0.7586209708627601, 'colsample_bytree': 0.8878167859532083, 'reg_alpha': 0.04208742378165828, 'reg_lambda': 0.020591250515067543, 'gamma': 2.32147737708232, 'max_delta_step': 4, 'scale_pos_weight': 0.6624656142930463}. Best is trial 23 with value: 0.8780184174141386.\n[I 2025-09-11 14:44:34,576] Trial 24 finished with value: 0.8775608615003389 and parameters: {'learning_rate': 0.07370106842585933, 'max_depth': 2, 'min_child_weight': 8, 'subsample': 0.7472332510838446, 'colsample_bytree': 0.881170833900878, 'reg_alpha': 0.045778122573147194, 'reg_lambda': 0.008440921464708082, 'gamma': 2.499498179033538, 'max_delta_step': 5, 'scale_pos_weight': 0.6597733124391032}. Best is trial 23 with value: 0.8780184174141386.\n[I 2025-09-11 14:44:37,566] Trial 25 finished with value: 0.8757177345834559 and parameters: {'learning_rate': 0.07304540833419661, 'max_depth': 2, 'min_child_weight': 10, 'subsample': 0.7458801374914791, 'colsample_bytree': 0.8764257725564044, 'reg_alpha': 0.2226687202233503, 'reg_lambda': 0.015994669366300507, 'gamma': 3.6655312184005204, 'max_delta_step': 10, 'scale_pos_weight': 0.8034089763170278}. Best is trial 23 with value: 0.8780184174141386.\n[I 2025-09-11 14:44:41,971] Trial 26 finished with value: 0.8737781663229207 and parameters: {'learning_rate': 0.06139438471552505, 'max_depth': 2, 'min_child_weight': 8, 'subsample': 0.9987756712764764, 'colsample_bytree': 0.8894378401586234, 'reg_alpha': 0.06165517848672096, 'reg_lambda': 4.130317117198479, 'gamma': 2.5959863147875115, 'max_delta_step': 5, 'scale_pos_weight': 0.6797085270506662}. Best is trial 23 with value: 0.8780184174141386.\n[I 2025-09-11 14:44:45,981] Trial 27 finished with value: 0.876043790080629 and parameters: {'learning_rate': 0.07412310390762915, 'max_depth': 2, 'min_child_weight': 8, 'subsample': 0.8044857349558138, 'colsample_bytree': 0.8393571995150907, 'reg_alpha': 0.7454723899475499, 'reg_lambda': 0.017048608314750854, 'gamma': 3.50814687515391, 'max_delta_step': 7, 'scale_pos_weight': 0.9569786693074536}. Best is trial 23 with value: 0.8780184174141386.\n[I 2025-09-11 14:44:49,765] Trial 28 finished with value: 0.8740407184653037 and parameters: {'learning_rate': 0.06667354721021977, 'max_depth': 3, 'min_child_weight': 6, 'subsample': 0.7551608009677344, 'colsample_bytree': 0.952842693890803, 'reg_alpha': 0.0008853064464378511, 'reg_lambda': 0.040123827333178964, 'gamma': 2.5164213450848827, 'max_delta_step': 3, 'scale_pos_weight': 0.6467670380826146}. Best is trial 23 with value: 0.8780184174141386.\n[I 2025-09-11 14:44:53,508] Trial 29 finished with value: 0.8668705669809436 and parameters: {'learning_rate': 0.05596303598848273, 'max_depth': 3, 'min_child_weight': 9, 'subsample': 0.8306610267446913, 'colsample_bytree': 0.8699296075183, 'reg_alpha': 0.04618775294080142, 'reg_lambda': 0.473057764142341, 'gamma': 4.619260887334414, 'max_delta_step': 5, 'scale_pos_weight': 1.7923613839904302}. Best is trial 23 with value: 0.8780184174141386.\n[I 2025-09-11 14:44:57,352] Trial 30 finished with value: 0.8776442840104162 and parameters: {'learning_rate': 0.07707207223894097, 'max_depth': 2, 'min_child_weight': 1, 'subsample': 0.7218411712353623, 'colsample_bytree': 0.8992223050944572, 'reg_alpha': 0.21658309410377868, 'reg_lambda': 0.005627075214154162, 'gamma': 3.206260884111363, 'max_delta_step': 7, 'scale_pos_weight': 0.8027761794403937}. Best is trial 23 with value: 0.8780184174141386.\n[I 2025-09-11 14:45:00,771] Trial 31 finished with value: 0.8775089765197416 and parameters: {'learning_rate': 0.0802025915479029, 'max_depth': 2, 'min_child_weight': 1, 'subsample': 0.7195722589358238, 'colsample_bytree': 0.8960727487875251, 'reg_alpha': 0.16913566231106225, 'reg_lambda': 0.006918262971123765, 'gamma': 4.070757795333474, 'max_delta_step': 7, 'scale_pos_weight': 0.80175619898617}. Best is trial 23 with value: 0.8780184174141386.\n[I 2025-09-11 14:45:04,046] Trial 32 finished with value: 0.8779579699161066 and parameters: {'learning_rate': 0.07733379227617934, 'max_depth': 2, 'min_child_weight': 1, 'subsample': 0.726416155077667, 'colsample_bytree': 0.9380106280101387, 'reg_alpha': 0.13405725458273623, 'reg_lambda': 0.03045358727353852, 'gamma': 3.2422639978837546, 'max_delta_step': 6, 'scale_pos_weight': 0.6283603967750532}. Best is trial 23 with value: 0.8780184174141386.\n[I 2025-09-11 14:45:07,461] Trial 33 finished with value: 0.8779220304572588 and parameters: {'learning_rate': 0.07843405983902367, 'max_depth': 2, 'min_child_weight': 1, 'subsample': 0.7253513447499258, 'colsample_bytree': 0.9696854859715777, 'reg_alpha': 0.09412973002193455, 'reg_lambda': 0.04422431836554154, 'gamma': 3.2896120760426584, 'max_delta_step': 6, 'scale_pos_weight': 0.6126682204681352}. Best is trial 23 with value: 0.8780184174141386.\n[I 2025-09-11 14:45:11,521] Trial 34 finished with value: 0.8757680287509688 and parameters: {'learning_rate': 0.07660577705690143, 'max_depth': 2, 'min_child_weight': 1, 'subsample': 0.7879650487061711, 'colsample_bytree': 0.9708981520696239, 'reg_alpha': 0.9859524827785663, 'reg_lambda': 0.04349562767542223, 'gamma': 3.319194027603105, 'max_delta_step': 8, 'scale_pos_weight': 0.9900283040456047}. Best is trial 23 with value: 0.8780184174141386.\n[I 2025-09-11 14:45:15,693] Trial 35 finished with value: 0.8781279523731775 and parameters: {'learning_rate': 0.050404101395776654, 'max_depth': 2, 'min_child_weight': 1, 'subsample': 0.7281230856878693, 'colsample_bytree': 0.9391926243571838, 'reg_alpha': 0.10961084886796146, 'reg_lambda': 0.17505415924376125, 'gamma': 3.0401175175181785, 'max_delta_step': 6, 'scale_pos_weight': 0.8462066972622363}. Best is trial 35 with value: 0.8781279523731775.\n[I 2025-09-11 14:45:20,121] Trial 36 finished with value: 0.8758785426719056 and parameters: {'learning_rate': 0.04621652031907384, 'max_depth': 2, 'min_child_weight': 3, 'subsample': 0.8877195268471448, 'colsample_bytree': 0.9483718651642056, 'reg_alpha': 0.10856915806948794, 'reg_lambda': 0.39966392339949125, 'gamma': 4.197095757279578, 'max_delta_step': 6, 'scale_pos_weight': 0.6041995920301092}. Best is trial 35 with value: 0.8781279523731775.\n[I 2025-09-11 14:45:24,628] Trial 37 finished with value: 0.8772945906624127 and parameters: {'learning_rate': 0.049721485070778025, 'max_depth': 2, 'min_child_weight': 2, 'subsample': 0.7940266622240626, 'colsample_bytree': 0.969221204167724, 'reg_alpha': 0.09267168685787606, 'reg_lambda': 1.7230607403173444, 'gamma': 2.8396821442439553, 'max_delta_step': 6, 'scale_pos_weight': 0.8671761492982359}. Best is trial 35 with value: 0.8781279523731775.\n[I 2025-09-11 14:45:29,434] Trial 38 finished with value: 0.8719864417040324 and parameters: {'learning_rate': 0.03357223213572251, 'max_depth': 3, 'min_child_weight': 3, 'subsample': 0.731622186432801, 'colsample_bytree': 0.9363675232455364, 'reg_alpha': 0.3691481163106855, 'reg_lambda': 0.1494086861389436, 'gamma': 2.886649676534807, 'max_delta_step': 9, 'scale_pos_weight': 1.226863240250415}. Best is trial 35 with value: 0.8781279523731775.\n[I 2025-09-11 14:45:32,849] Trial 39 finished with value: 0.8784110865096325 and parameters: {'learning_rate': 0.06752158231148221, 'max_depth': 2, 'min_child_weight': 2, 'subsample': 0.7585878669161433, 'colsample_bytree': 0.9930739168635009, 'reg_alpha': 0.09116072344281263, 'reg_lambda': 0.24538541468790598, 'gamma': 3.3229439701646273, 'max_delta_step': 6, 'scale_pos_weight': 0.5937986672161575}. Best is trial 39 with value: 0.8784110865096325.\n[I 2025-09-11 14:45:36,781] Trial 40 finished with value: 0.8732434661720498 and parameters: {'learning_rate': 0.051466505994509144, 'max_depth': 3, 'min_child_weight': 2, 'subsample': 0.7621541930286028, 'colsample_bytree': 0.9875215615053798, 'reg_alpha': 6.640659238240391e-05, 'reg_lambda': 0.25944534522085266, 'gamma': 4.330636737553637, 'max_delta_step': 7, 'scale_pos_weight': 0.7260034184582022}. Best is trial 39 with value: 0.8784110865096325.\n[I 2025-09-11 14:45:40,605] Trial 41 finished with value: 0.8776730954029438 and parameters: {'learning_rate': 0.06800629132227162, 'max_depth': 2, 'min_child_weight': 1, 'subsample': 0.755041998237342, 'colsample_bytree': 0.9695216005431493, 'reg_alpha': 0.09145123110547697, 'reg_lambda': 0.08275130636070918, 'gamma': 3.4165705873982666, 'max_delta_step': 6, 'scale_pos_weight': 0.5647440636410177}. Best is trial 39 with value: 0.8784110865096325.\n[I 2025-09-11 14:45:44,639] Trial 42 finished with value: 0.8778733135061371 and parameters: {'learning_rate': 0.060633841438734584, 'max_depth': 2, 'min_child_weight': 2, 'subsample': 0.7779213467750966, 'colsample_bytree': 0.9386031229363153, 'reg_alpha': 0.3536807180896481, 'reg_lambda': 0.8440180710259424, 'gamma': 3.0733726281024194, 'max_delta_step': 6, 'scale_pos_weight': 0.6094181759628182}. Best is trial 39 with value: 0.8784110865096325.\n[I 2025-09-11 14:45:49,162] Trial 43 finished with value: 0.8776240963204419 and parameters: {'learning_rate': 0.042140310829939534, 'max_depth': 2, 'min_child_weight': 1, 'subsample': 0.7346098498505383, 'colsample_bytree': 0.9948363774554119, 'reg_alpha': 0.13197145432818055, 'reg_lambda': 0.02633433503133801, 'gamma': 2.7396310566095075, 'max_delta_step': 6, 'scale_pos_weight': 0.7207292245042858}. Best is trial 39 with value: 0.8784110865096325.\n[I 2025-09-11 14:45:52,968] Trial 44 finished with value: 0.8793771519443567 and parameters: {'learning_rate': 0.07075450573311484, 'max_depth': 2, 'min_child_weight': 3, 'subsample': 0.7122711670540267, 'colsample_bytree': 0.9640910748945313, 'reg_alpha': 0.05991271584983177, 'reg_lambda': 0.08968507825649656, 'gamma': 2.2508451539938656, 'max_delta_step': 5, 'scale_pos_weight': 0.5737624832445724}. Best is trial 44 with value: 0.8793771519443567.\n[I 2025-09-11 14:45:56,154] Trial 45 finished with value: 0.8754326323256849 and parameters: {'learning_rate': 0.07079758272142457, 'max_depth': 2, 'min_child_weight': 3, 'subsample': 0.7123644982701529, 'colsample_bytree': 0.9592253174877994, 'reg_alpha': 0.028384011374965608, 'reg_lambda': 0.10777293017184539, 'gamma': 2.1829198470492943, 'max_delta_step': 5, 'scale_pos_weight': 0.8836431787558745}. Best is trial 44 with value: 0.8793771519443567.\n[I 2025-09-11 14:45:59,871] Trial 46 finished with value: 0.874003062423961 and parameters: {'learning_rate': 0.06415124587925924, 'max_depth': 2, 'min_child_weight': 2, 'subsample': 0.8448441270874604, 'colsample_bytree': 0.912090118050177, 'reg_alpha': 0.06312644363600925, 'reg_lambda': 1.2069680919260903, 'gamma': 2.3098952954880265, 'max_delta_step': 3, 'scale_pos_weight': 1.3260583725015198}. Best is trial 44 with value: 0.8793771519443567.\n[I 2025-09-11 14:46:03,442] Trial 47 finished with value: 0.872761191470324 and parameters: {'learning_rate': 0.058353985276213924, 'max_depth': 2, 'min_child_weight': 4, 'subsample': 0.8046044222893126, 'colsample_bytree': 0.7934217643529313, 'reg_alpha': 0.0068460021270454596, 'reg_lambda': 0.25506315953666886, 'gamma': 3.7443564664744944, 'max_delta_step': 5, 'scale_pos_weight': 1.5082840060731657}. Best is trial 44 with value: 0.8793771519443567.\n[I 2025-09-11 14:46:07,310] Trial 48 finished with value: 0.8690182938134006 and parameters: {'learning_rate': 0.06847770948354315, 'max_depth': 4, 'min_child_weight': 3, 'subsample': 0.7114063230934565, 'colsample_bytree': 0.980223431042431, 'reg_alpha': 0.2513423257774865, 'reg_lambda': 0.10903531744371808, 'gamma': 2.7037534347849763, 'max_delta_step': 1, 'scale_pos_weight': 0.5928948558756737}. Best is trial 44 with value: 0.8793771519443567.\n[I 2025-09-11 14:46:10,320] Trial 49 finished with value: 0.8763548620220848 and parameters: {'learning_rate': 0.09335717890663457, 'max_depth': 2, 'min_child_weight': 2, 'subsample': 0.7529278816168745, 'colsample_bytree': 0.9319449496500718, 'reg_alpha': 0.013801619150474582, 'reg_lambda': 0.06519826125420747, 'gamma': 3.13266248446876, 'max_delta_step': 4, 'scale_pos_weight': 0.786707807494433}. Best is trial 44 with value: 0.8793771519443567.\n","output_type":"stream"},{"name":"stdout","text":"Best parameters: {'learning_rate': 0.07075450573311484, 'max_depth': 2, 'min_child_weight': 3, 'subsample': 0.7122711670540267, 'colsample_bytree': 0.9640910748945313, 'reg_alpha': 0.05991271584983177, 'reg_lambda': 0.08968507825649656, 'gamma': 2.2508451539938656, 'max_delta_step': 5, 'scale_pos_weight': 0.5737624832445724}\nBest AUC: 0.8793771519443567\n[0]\tvalidation_0-auc:0.56935\n[100]\tvalidation_0-auc:0.80408\n[200]\tvalidation_0-auc:0.83906\n[300]\tvalidation_0-auc:0.85514\n[400]\tvalidation_0-auc:0.86535\n[500]\tvalidation_0-auc:0.87269\n[600]\tvalidation_0-auc:0.87781\n[700]\tvalidation_0-auc:0.88147\n[800]\tvalidation_0-auc:0.88430\n[900]\tvalidation_0-auc:0.88670\n[1000]\tvalidation_0-auc:0.88853\n[1100]\tvalidation_0-auc:0.88977\n[1200]\tvalidation_0-auc:0.89104\n[1300]\tvalidation_0-auc:0.89205\n[1400]\tvalidation_0-auc:0.89307\n[1500]\tvalidation_0-auc:0.89365\n[1600]\tvalidation_0-auc:0.89437\n[1700]\tvalidation_0-auc:0.89500\n[1800]\tvalidation_0-auc:0.89546\n[1900]\tvalidation_0-auc:0.89605\n[2000]\tvalidation_0-auc:0.89657\n[2100]\tvalidation_0-auc:0.89695\n[2200]\tvalidation_0-auc:0.89706\n[2300]\tvalidation_0-auc:0.89736\n[2400]\tvalidation_0-auc:0.89753\n[2500]\tvalidation_0-auc:0.89766\n[2600]\tvalidation_0-auc:0.89762\n[2605]\tvalidation_0-auc:0.89763\nFinal model AUC: 0.8976918940547582\n","output_type":"stream"}],"execution_count":14}]}